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Zhang X, Wang Y, Tang Y, Wang Z. Adaptive filter of frequency bands based coordinate attention network for EEG-based motor imagery classification. Health Inf Sci Syst 2024; 12:11. [PMID: 38404713 PMCID: PMC10890995 DOI: 10.1007/s13755-024-00270-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 01/03/2024] [Indexed: 02/27/2024] Open
Abstract
Purpose In the brain-computer interface (BCI), motor imagery (MI) could be defined as the Electroencephalogram (EEG) signals through imagined movements, and ultimately enabling individuals to control external devices. However, the low signal-to-noise ratio, multiple channels and non-linearity are the essential challenges of accurate MI classification. To tackle these issues, we investigate the role of adaptive frequency bands selection and spatial-temporal feature learning in decoding motor imagery. Methods We propose an Adaptive Filter of Frequency Bands based Coordinate Attention Network (AFFB-CAN) to improve the performance of MI classification. Specifically, we design the AFFB to adaptively obtain the upper and lower limits of frequency bands in order to alleviate information loss caused by manual selection. Next, we propose the CAN-based network to emphasize the key brain regions and temporal segments. And we design a multi-scale module to enhance temporal context learning. Results The conducted experiments on the BCI Competition IV-2a and 2b datasets reveal that our approach achieves an outstanding average accuracy, kappa values, and Macro F1-Score with 0.7825, 0.7104, and 0.7486 respectively. Similarly, for the BCI Competition IV-2b dataset, the average accuracy, kappa values, and F1-Score obtained are 0.8879, 0.7427, and 0.8734, respectively. Conclusion The proposed AFFB-CAN method improves the performance of MI classification. In addition, our study confirms previous findings that motor imagery is mainly associated with µ and β rhythms. Moreover, we also find that γ rhythms also play an important role in MI classification.
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Affiliation(s)
- Xiaoli Zhang
- The School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093 China
| | - Yongxionga Wang
- The School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093 China
| | - Yiheng Tang
- The School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093 China
| | - Zhe Wang
- The School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093 China
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2
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Zhang D, Li H, Xie J. Unsupervised and semi-supervised domain adaptation networks considering both global knowledge and prototype-based local class information for Motor Imagery Classification. Neural Netw 2024; 179:106497. [PMID: 38986186 DOI: 10.1016/j.neunet.2024.106497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 05/21/2024] [Accepted: 06/25/2024] [Indexed: 07/12/2024]
Abstract
The non-stationarity of EEG signals results in variability across sessions, impeding model building and data sharing. In this paper, we propose a domain adaptation method called GPL, which simultaneously considers global knowledge and prototype-based local class information to enhance the classification accuracy of motor imagery signals. Depending on the amount of labeled data available in the target domain, the method is implemented in both unsupervised and semi-supervised versions. Specifically, at the global level, we employ the maximum mean difference (MMD) loss to globally constrain the feature space, achieving comprehensive alignment. In the context of class-level operations, we propose two memory banks designed to accommodate class prototypes in each domain and constrain feature embeddings by applying two prototype-based contrastive losses. The source contrastive loss is used to organize source features spatially based on categories, thereby reconciling inter-class and intra-class relationships, while the interactive contrastive loss is employed to facilitate cross-domain information interaction. Simultaneously, in unsupervised scenarios, to mitigate the adverse effects of excessive pseudo-labels, we introduce an entropy-aware strategy that dynamically evaluates the confidence level of target data and personalized constraints on the participation of interactive contrastive loss. To validate our approach, extensive experiments were conducted on a highly regarded public EEG dataset, namely Dataset IIa of the BCI Competition IV, as well as a large-scale EEG dataset called GigaDB. The experiments yielded average classification accuracies of 86.03% and 84.22% respectively. These results demonstrate that our method is an effective EEG decoding model, conducive to advancing the development of motor imagery brain-computer interfaces. The architecture proposed in this study and the code for data partitioning can be found at https://github.com/zhangdx21/GPL.
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Affiliation(s)
- Dongxue Zhang
- Jilin University, College of Computer Science and Technology, Changchun, Jilin Province, China; Key Laboratory of Symbol Computation and Knowledge Engineering, Jilin University, Changchun 130012, China
| | - Huiying Li
- Jilin University, College of Computer Science and Technology, Changchun, Jilin Province, China; Key Laboratory of Symbol Computation and Knowledge Engineering, Jilin University, Changchun 130012, China.
| | - Jingmeng Xie
- Xi'an Jiaotong University, College of Electronic information, Xi'an, Shanxi Province, China
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3
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Asish SM, Kulshreshth AK, Borst CW, Sutradhar S. Classification of Internal and External Distractions in an Educational VR Environment Using Multimodal Features. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024; 30:7332-7342. [PMID: 39255100 DOI: 10.1109/tvcg.2024.3456207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Virtual reality (VR) can potentially enhance student engagement and memory retention in the classroom. However, distraction among participants in a VR-based classroom is a significant concern. Several factors, including mind wandering, external noise, stress, etc., can cause students to become internally and/or externally distracted while learning. To detect distractions, single or multi-modal features can be used. A single modality is found to be insufficient to detect both internal and external distractions, mainly because of individual variability. In this work, we investigated multi-modal features: eye tracking and EEG data, to classify the internal and external distractions in an educational VR environment. We set up our educational VR environment and equipped it for multi-modal data collection. We implemented different machine learning (ML) methods, including k-nearest-neighbors (kNN), Random Forest (RF), one-dimensional convolutional neural network - long short-term memory (1 D-CNN-LSTM), and two-dimensional convolutional neural networks (2D-CNN) to classify participants' internal and external distraction states using the multi-modal features. We performed cross-subject, cross-session, and gender-based grouping tests to evaluate our models. We found that the RF classifier achieves the highest accuracy over 83% in the cross-subject test, around 68% to 78% in the cross-session test, and around 90% in the gender-based grouping test compared to other models. SHAP analysis of the extracted features illustrated greater contributions from the occipital and prefrontal regions of the brain, as well as gaze angle, gaze origin, and head rotation features from the eye tracking data.
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4
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Meenakshinathan J, Gupta V, Reddy TK, Behera L, Sandhan T. Session-independent subject-adaptive mental imagery BCI using selective filter-bank adaptive Riemannian features. Med Biol Eng Comput 2024; 62:3293-3310. [PMID: 38825665 DOI: 10.1007/s11517-024-03137-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 05/23/2024] [Indexed: 06/04/2024]
Abstract
The brain-computer interfaces (BCIs) facilitate the users to exploit information encoded in neural signals, specifically electroencephalogram (EEG), to control devices and for neural rehabilitation. Mental imagery (MI)-driven BCI predicts the user's pre-meditated mental objectives, which could be deployed as command signals. This paper presents a novel learning-based framework for classifying MI tasks using EEG-based BCI. In particular, our work focuses on the variation in inter-session data and the extraction of multi-spectral user-tailored features for robust performance. Thus, the goal is to create a calibration-free subject-adaptive learning framework for various mental imagery tasks not restricted to motor imagery alone. In this regard, critical spectral bands and the best temporal window are first selected from the EEG training trials of the subject based on the Riemannian user learning distance metric (Dscore) that checks for distinct and stable patterns. The filtered covariance matrices of the EEG trials in each spectral band are then transformed towards a reference covariance matrix using the Riemannian transfer learning, enabling the different sessions to be comparable. The evaluation of our proposed Selective Time-window and Multi-scale Filter-Bank with Adaptive Riemannian (STFB-AR) features on four public datasets, including disabled subjects, showed around 15% and 8% improvement in mean accuracy over baseline and fixed filter-bank models, respectively.
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Affiliation(s)
| | - Vinay Gupta
- Department of Electrical Engineering, IIT Kanpur, Kanpur, India
| | | | - Laxmidhar Behera
- Department of Electrical Engineering, IIT Kanpur, Kanpur, India
- IIT Mandi, Mandi, India
| | - Tushar Sandhan
- Department of Electrical Engineering, IIT Kanpur, Kanpur, India
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5
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Kim N, Lee S, Kim J, Choi SY, Park SM. Shuffled ECA-Net for stress detection from multimodal wearable sensor data. Comput Biol Med 2024; 183:109217. [PMID: 39366142 DOI: 10.1016/j.compbiomed.2024.109217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 08/14/2024] [Accepted: 09/25/2024] [Indexed: 10/06/2024]
Abstract
BACKGROUND Recently, stress has been recognized as a key factor in the emergence of individual and social issues. Numerous attempts have been made to develop sensor-augmented psychological stress detection techniques, although existing methods are often impractical or overly subjective. To overcome these limitations, we acquired a dataset utilizing both wireless wearable multimodal sensors and salivary cortisol tests for supervised learning. We also developed a novel deep neural network (DNN) model that maximizes the benefits of sensor fusion. METHOD We devised a DNN involving a shuffled efficient channel attention (ECA) module called a shuffled ECA-Net, which achieves advanced feature-level sensor fusion by considering inter-modality relationships. Through an experiment involving salivary cortisol tests on 26 participants, we acquired multiple bio-signals including electrocardiograms, respiratory waveforms, and electrogastrograms in both relaxed and stressed mental states. A training dataset was generated from the obtained data. Using the dataset, our proposed model was optimized and evaluated ten times through five-fold cross-validation, while varying a random seed. RESULTS Our proposed model achieved acceptable performance in stress detection, showing 0.916 accuracy, 0.917 sensitivity, 0.916 specificity, 0.914 F1-score, and 0.964 area under the receiver operating characteristic curve (AUROC). Furthermore, we demonstrated that combining multiple bio-signals with a shuffled ECA module can more accurately detect psychological stress. CONCLUSIONS We believe that our proposed model, coupled with the evidence for the viability of multimodal sensor fusion and a shuffled ECA-Net, would significantly contribute to the resolution of stress-related issues.
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Affiliation(s)
- Namho Kim
- Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
| | - Seongjae Lee
- Major of Medical Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
| | - Junho Kim
- School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
| | - So Yoon Choi
- Department of Pediatrics, Kosin University Gospel Hospital, Kosin University College of Medicine, Busan, Republic of Korea.
| | - Sung-Min Park
- Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea; Major of Medical Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea; Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea; Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
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6
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Fu B, Chu W, Gu C, Liu Y. Cross-Modal Guiding Neural Network for Multimodal Emotion Recognition From EEG and Eye Movement Signals. IEEE J Biomed Health Inform 2024; 28:5865-5876. [PMID: 38917288 DOI: 10.1109/jbhi.2024.3419043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
Multimodal emotion recognition research is gaining attention because of the emerging trend of integrating information from different sensory modalities to improve performance. Electroencephalogram (EEG) signals are considered objective indicators of emotions and provide precise insights despite their complex data collection. In contrast, eye movement signals are more susceptible to environmental and individual differences but offer convenient data collection. Conventional emotion recognition methods typically use separate models for different modalities, potentially overlooking their inherent connections. This study introduces a cross-modal guiding neural network designed to fully leverage the strengths of both modalities. The network includes a dual-branch feature extraction module that simultaneously extracts features from EEG and eye movement signals. In addition, the network includes a feature guidance module that uses EEG features to direct eye movement feature extraction, reducing the impact of subjective factors. This study also introduces a feature reweighting module to explore emotion-related features within eye movement signals, thereby improving emotion classification accuracy. The empirical findings from both the SEED-IV dataset and our collected dataset substantiate the commendable performance of the model, thereby confirming its efficacy.
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7
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Li D, Wang J, Xu J, Fang X, Ji Y. Cross-Channel Specific-Mutual Feature Transfer Learning for Motor Imagery EEG Signals Decoding. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:13472-13482. [PMID: 37220058 DOI: 10.1109/tnnls.2023.3269512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
In recent years, with the rapid development of deep learning, various deep learning frameworks have been widely used in brain-computer interface (BCI) research for decoding motor imagery (MI) electroencephalogram (EEG) signals to understand brain activity accurately. The electrodes, however, record the mixed activities of neurons. If different features are directly embedded in the same feature space, the specific and mutual features of different neuron regions are not considered, which will reduce the expression ability of the feature itself. We propose a cross-channel specific-mutual feature transfer learning (CCSM-FT) network model to solve this problem. The multibranch network extracts the specific and mutual features of brain's multiregion signals. Effective training tricks are used to maximize the distinction between the two kinds of features. Suitable training tricks can also improve the effectiveness of the algorithm compared with novel models. Finally, we transfer two kinds of features to explore the potential of mutual and specific features to enhance the expressive power of the feature and use the auxiliary set to improve identification performance. The experimental results show that the network has a better classification effect in the BCI Competition IV-2a and the HGD datasets.
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8
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Wang X, Yang W, Qi W, Wang Y, Ma X, Wang W. STaRNet: A spatio-temporal and Riemannian network for high-performance motor imagery decoding. Neural Netw 2024; 178:106471. [PMID: 38945115 DOI: 10.1016/j.neunet.2024.106471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 06/11/2024] [Accepted: 06/16/2024] [Indexed: 07/02/2024]
Abstract
Brain-computer interfaces (BCIs), representing a transformative form of human-computer interaction, empower users to interact directly with external environments through brain signals. In response to the demands for high accuracy, robustness, and end-to-end capabilities within BCIs based on motor imagery (MI), this paper introduces STaRNet, a novel model that integrates multi-scale spatio-temporal convolutional neural networks (CNNs) with Riemannian geometry. Initially, STaRNet integrates a multi-scale spatio-temporal feature extraction module that captures both global and local features, facilitating the construction of Riemannian manifolds from these comprehensive spatio-temporal features. Subsequently, a matrix logarithm operation transforms the manifold-based features into the tangent space, followed by a dense layer for classification. Without preprocessing, STaRNet surpasses state-of-the-art (SOTA) models by achieving an average decoding accuracy of 83.29% and a kappa value of 0.777 on the BCI Competition IV 2a dataset, and 95.45% accuracy with a kappa value of 0.939 on the High Gamma Dataset. Additionally, a comparative analysis between STaRNet and several SOTA models, focusing on the most challenging subjects from both datasets, highlights exceptional robustness of STaRNet. Finally, the visualizations of learned frequency bands demonstrate that temporal convolutions have learned MI-related frequency bands, and the t-SNE analyses of features across multiple layers of STaRNet exhibit strong feature extraction capabilities. We believe that the accurate, robust, and end-to-end capabilities of the STaRNet will facilitate the advancement of BCIs.
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Affiliation(s)
- Xingfu Wang
- CAS Key Laboratory of Space Manufacturing Technology, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Wenjie Yang
- CAS Key Laboratory of Space Manufacturing Technology, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Wenxia Qi
- CAS Key Laboratory of Space Manufacturing Technology, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Yu Wang
- National Engineering and Technology Research Center for ASIC Design, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Xiaojun Ma
- National Engineering and Technology Research Center for ASIC Design, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Wei Wang
- CAS Key Laboratory of Space Manufacturing Technology, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
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9
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Sun H, Ding Y, Bao J, Qin K, Tong C, Jin J, Guan C. Leveraging temporal dependency for cross-subject-MI BCIs by contrastive learning and self-attention. Neural Netw 2024; 178:106470. [PMID: 38943861 DOI: 10.1016/j.neunet.2024.106470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/29/2024] [Accepted: 06/16/2024] [Indexed: 07/01/2024]
Abstract
Brain-computer interfaces (BCIs) built based on motor imagery paradigm have found extensive utilization in motor rehabilitation and the control of assistive applications. However, traditional MI-BCI systems often exhibit suboptimal classification performance and require significant time for new users to collect subject-specific training data. This limitation diminishes the user-friendliness of BCIs and presents significant challenges in developing effective subject-independent models. In response to these challenges, we propose a novel subject-independent framework for learning temporal dependency for motor imagery BCIs by Contrastive Learning and Self-attention (CLS). In CLS model, we incorporate self-attention mechanism and supervised contrastive learning into a deep neural network to extract important information from electroencephalography (EEG) signals as features. We evaluate the CLS model using two large public datasets encompassing numerous subjects in a subject-independent experiment condition. The results demonstrate that CLS outperforms six baseline algorithms, achieving a mean classification accuracy improvement of 1.3 % and 4.71 % than the best algorithm on the Giga dataset and OpenBMI dataset, respectively. Our findings demonstrate that CLS can effectively learn invariant discriminative features from training data obtained from non-target subjects, thus showcasing its potential for building models for new users without the need for calibration.
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Affiliation(s)
- Hao Sun
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Yi Ding
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Jianzhu Bao
- School of Computer Science and Technology, Harbin Insitute of Technology, Shenzhen, China
| | - Ke Qin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Chengxuan Tong
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Jing Jin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China; Shenzhen Research Institute of East China University of Technology, Shen Zhen 518063, China.
| | - Cuntai Guan
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore.
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10
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Yu H, Baek S, Lee J, Sohn I, Hwang B, Park C. Deep Neural Network-Based Empirical Mode Decomposition for Motor Imagery EEG Classification. IEEE Trans Neural Syst Rehabil Eng 2024; 32:3647-3656. [PMID: 39037874 DOI: 10.1109/tnsre.2024.3432102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Motor imagery refers to the brain's response during the mental simulation of physical activities, which can be detected through electroencephalogram (EEG) signals. However, EEG signals exhibit a low signal-to-noise ratio (SNR) due to various artifacts originating from other physiological sources. To enhance the classification performance of motor imagery tasks by increasing the SNR of EEG signals, several signal decomposition approaches have been proposed. Empirical mode decomposition (EMD) has shown promising results in extracting EEG components associated with motor imagery tasks more effectively than traditional linear decomposition algorithms such as Fourier and wavelet methods. Nevertheless, the EMD-based algorithm suffers from a significant challenge known as mode mixing, where frequency components intertwine with the intrinsic mode functions obtained through EMD. This issue severely hampers the accuracy of motor imagery classification. Despite numerous algorithms proposed, mode mixing remains a persistent issue. In this paper, we propose the Deep-EMD algorithm, a deep neural network-based approach to mode mixing problem. We employ two datasets to compare the motor imagery classification and mode mixing improvement achieved by the conventional EMD algorithms. Our experimental results demonstrate that the Deep-EMD algorithm effectively mitigates the mode mixing problem in decomposed EEG components, leading to improved motor imagery classification performance.
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Si X, Huang D, Liang Z, Sun Y, Huang H, Liu Q, Yang Z, Ming D. Temporal aware Mixed Attention-based Convolution and Transformer Network for cross-subject EEG emotion recognition. Comput Biol Med 2024; 181:108973. [PMID: 39213709 DOI: 10.1016/j.compbiomed.2024.108973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 07/15/2024] [Accepted: 07/29/2024] [Indexed: 09/04/2024]
Abstract
Emotion recognition is crucial for human-computer interaction, and electroencephalography (EEG) stands out as a valuable tool for capturing and reflecting human emotions. In this study, we propose a hierarchical hybrid model called Mixed Attention-based Convolution and Transformer Network (MACTN). This model is designed to collectively capture both local and global temporal information and is inspired by insights from neuroscientific research on the temporal dynamics of emotions. First, we introduce depth-wise temporal convolution and separable convolution to extract local temporal features. Then, a self-attention-based transformer is used to integrate the sparse global emotional features. Besides, channel attention mechanism is designed to identify the most task-relevant channels, facilitating the capture of relationships between different channels and emotional states. Extensive experiments are conducted on three public datasets under both offline and online evaluation modes. In the multi-class cross-subject online evaluation using the THU-EP dataset, MACTN demonstrates an approximate 8% enhancement in 9-class emotion recognition accuracy in comparison to state-of-the-art methods. In the multi-class cross-subject offline evaluation using the DEAP and SEED datasets, a comparable performance is achieved solely based on the raw EEG signals, without the need for prior knowledge or transfer learning during the feature extraction and learning process. Furthermore, ablation studies have shown that integrating self-attention and channel-attention mechanisms improves classification performance. This method won the Emotional BCI Competition's final championship in the World Robot Contest. The source code is available at https://github.com/ThreePoundUniverse/MACTN.
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Affiliation(s)
- Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, State Key Laboratory of Advanced Medical Materials and Devices, Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin Key Laboratory of Brain Science and Neural Engineering, Institute of Applied Psychology, Tianjin University, Tianjin 300072, China
| | - Dong Huang
- Academy of Medical Engineering and Translational Medicine, State Key Laboratory of Advanced Medical Materials and Devices, Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin Key Laboratory of Brain Science and Neural Engineering, Institute of Applied Psychology, Tianjin University, Tianjin 300072, China
| | - Zhen Liang
- School of Biomedical Engineering, Medical School, Shenzhen University, Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Yulin Sun
- Academy of Medical Engineering and Translational Medicine, State Key Laboratory of Advanced Medical Materials and Devices, Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin Key Laboratory of Brain Science and Neural Engineering, Institute of Applied Psychology, Tianjin University, Tianjin 300072, China
| | - He Huang
- Academy of Medical Engineering and Translational Medicine, State Key Laboratory of Advanced Medical Materials and Devices, Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin Key Laboratory of Brain Science and Neural Engineering, Institute of Applied Psychology, Tianjin University, Tianjin 300072, China
| | - Qile Liu
- School of Biomedical Engineering, Medical School, Shenzhen University, Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Zhuobin Yang
- Academy of Medical Engineering and Translational Medicine, State Key Laboratory of Advanced Medical Materials and Devices, Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin Key Laboratory of Brain Science and Neural Engineering, Institute of Applied Psychology, Tianjin University, Tianjin 300072, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, State Key Laboratory of Advanced Medical Materials and Devices, Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin Key Laboratory of Brain Science and Neural Engineering, Institute of Applied Psychology, Tianjin University, Tianjin 300072, China.
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12
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Li Z, Tan X, Li X, Yin L. Multiclass motor imagery classification with Riemannian geometry and temporal-spectral selection. Med Biol Eng Comput 2024; 62:2961-2973. [PMID: 38724769 DOI: 10.1007/s11517-024-03103-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 04/19/2024] [Indexed: 09/07/2024]
Abstract
Motor imagery (MI) based brain-computer interfaces (BCIs) decode the users' intentions from electroencephalography (EEG) to achieve information control and interaction between the brain and external devices. In this paper, firstly, we apply Riemannian geometry to the covariance matrix extracted by spatial filtering to obtain robust and distinct features. Then, a multiscale temporal-spectral segmentation scheme is developed to enrich the feature dimensionality. In order to determine the optimal feature configurations, we utilize a linear learning-based temporal window and spectral band (TWSB) selection method to evaluate the feature contributions, which efficiently reduces the redundant features and improves the decoding efficiency without excessive loss of accuracy. Finally, support vector machines are used to predict the classification labels based on the selected MI features. To evaluate the performance of our model, we test it on the publicly available BCI Competition IV dataset 2a and 2b. The results show that the method has an average accuracy of 79.1% and 83.1%, which outperforms other existing methods. Using TWSB feature selection instead of selecting all features improves the accuracy by up to about 6%. Moreover, the TWSB selection method can effectively reduce the computational burden. We believe that the framework reveals more interpretable feature information of motor imagery EEG signals, provides neural responses discriminative with high accuracy, and facilitates the performance of real-time MI-BCI.
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Affiliation(s)
- Zhaohui Li
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, 066004, China
| | - Xiaohui Tan
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China
| | - Xinyu Li
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China
| | - Liyong Yin
- Department of Neurology, The First Hospital of Qinhuangdao, No. 258 Wenhua Road, Haigang District, Qinhuangdao, 066004, Hebei, People's Republic of China.
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13
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Chen D, Huang H, Guan Z, Pan J, Li Y. An Intersubject Brain-Computer Interface Based on Domain-Adversarial Training of Convolutional Neural Network. IEEE Trans Biomed Eng 2024; 71:2956-2967. [PMID: 38781054 DOI: 10.1109/tbme.2024.3404131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
OBJECTIVE Attention decoding plays a vital role in daily life, where electroencephalography (EEG) has been widely involved. However, training a universally effective model for everyone is impractical due to substantial interindividual variability in EEG signals. To tackle the above challenge, we propose an end-to-end brain-computer interface (BCI) framework, including temporal and spatial one-dimensional (1D) convolutional neural network and domain-adversarial training strategy, namely DA-TSnet. METHOD Specifically, DA-TSnet extracts temporal and spatial features of EEG, while it is jointly supervised by task loss and domain loss. During training, DA-TSnet aims to maximize the domain loss while simultaneously minimizing the task loss. We conduct an offline analysis, simulate online experiments on a self-collected dataset of 85 subjects, and real online experiments on 22 subjects. MAIN RESULTS DA-TSnet achieves a leave-one-subject-out (LOSO) cross-validation (CV) classification accuracy of 89.40% ± 9.96%, outperforming several state-of-the-art attention EEG decoding methods. In simulated online experiments, DA-TSnet achieves an outstanding accuracy of 88.07% ± 11.22%. In real online experiments, it achieves an average accuracy surpassing 86%. SIGNIFICANCE An end-to-end network framework does not rely on elaborate preprocessing and feature extraction steps, which saves time and human workload. Moreover, our framework utilizes domain-adversarial training neural network (DANN) to tackle the challenge posed by the high interindividual variability in EEG signals, which has significant reference value for handling other EEG signal decoding issues. Last, the performance of the DA-TSnet framework in offline and online experiments underscores its potential to facilitate more reliable applications.
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Brenner A, Knispel F, Fischer FP, Rossmanith P, Weber Y, Koch H, Röhrig R, Varghese J, Kutafina E. Concept-based AI interpretability in physiological time-series data: Example of abnormality detection in electroencephalography. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 257:108448. [PMID: 39395304 DOI: 10.1016/j.cmpb.2024.108448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 09/02/2024] [Accepted: 09/29/2024] [Indexed: 10/14/2024]
Abstract
BACKGROUND AND OBJECTIVE Despite recent performance advancements, deep learning models are not yet adopted in clinical practice on a wide scale. The intrinsic intransparency of such systems is commonly cited as one major reason for this reluctance. This has motivated methods that aim to provide explanations of model functioning. Known limitations of feature-based explanations have led to an increased interest in concept-based interpretability. Testing with Concept Activation Vectors (TCAV) employs human-understandable, abstract concepts to explain model behavior. The method has previously been applied to the medical domain in the context of electronic health records, retinal fundus images and magnetic resonance imaging. METHODS We explore the usage of TCAV for building interpretable models on physiological time series, using an example of abnormality detection in electroencephalography (EEG). For this purpose, we adopt the XceptionTime model, which is suitable for multi-channel physiological data of variable sizes. The model provides state-of-the-art performance on raw EEG data and is publically available. We propose and test several ideas regarding concept definition through metadata mining, using additional labeled EEG data and extracting interpretable signal characteristics in the form of frequencies. By including our own hospital data with analog labeling, we further evaluate the robustness of our approach. RESULTS The tested concepts show a TCAV score distribution that is in line with the clinical expectations, i.e. concepts known to have strong links with EEG pathologies (such as epileptiform discharges) received higher scores than the neutral concepts (e.g. sex). The scores were consistent across the applied concept generation strategies. CONCLUSIONS TCAV has the potential to improve interpretability of deep learning applied to multi-channel signals as well as to detect possible biases in the data. Still, further work on developing the strategies for concept definition and validation on clinical physiological time series is needed to better understand how to extract clinically relevant information from the concept sensitivity scores.
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Affiliation(s)
- Alexander Brenner
- Institute of Medical Informatics, University of Münster, Münster, Germany.
| | - Felix Knispel
- Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Florian P Fischer
- Department of Epileptology and Neurology, Medical Faculty, RWTH Aachen University Hospital, Aachen, Germany
| | - Peter Rossmanith
- Theoretical Computer Science, Department of Computer Science, RWTH Aachen University, Aachen, Germany
| | - Yvonne Weber
- Department of Epileptology and Neurology, Medical Faculty, RWTH Aachen University Hospital, Aachen, Germany
| | - Henner Koch
- Department of Epileptology and Neurology, Medical Faculty, RWTH Aachen University Hospital, Aachen, Germany
| | - Rainer Röhrig
- Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Julian Varghese
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Ekaterina Kutafina
- Institute for Biomedical Informatics, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany
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Shi X, Li B, Wang W, Qin Y, Wang H, Wang X. EEG-VTTCNet: A loss joint training model based on the vision transformer and the temporal convolution network for EEG-based motor imagery classification. Neuroscience 2024; 556:42-51. [PMID: 39103043 DOI: 10.1016/j.neuroscience.2024.07.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 07/08/2024] [Accepted: 07/31/2024] [Indexed: 08/07/2024]
Abstract
Brain-computer interface (BCI) is a technology that directly connects signals between the human brain and a computer or other external device. Motor imagery electroencephalographic (MI-EEG) signals are considered a promising paradigm for BCI systems, with a wide range of potential applications in medical rehabilitation, human-computer interaction, and virtual reality. Accurate decoding of MI-EEG signals poses a significant challenge due to issues related to the quality of the collected EEG data and subject variability. Therefore, developing an efficient MI-EEG decoding network is crucial and warrants research. This paper proposes a loss joint training model based on the vision transformer (VIT) and the temporal convolutional network (EEG-VTTCNet) to classify MI-EEG signals. To take advantage of multiple modules together, the EEG-VTTCNet adopts a shared convolution strategy and a dual-branching strategy. The dual-branching modules perform complementary learning and jointly train shared convolutional modules with better performance. We conducted experiments on the BCI Competition IV-2a and IV-2b datasets, and the proposed network outperformed the current state-of-the-art techniques with an accuracy of 84.58% and 90.94%, respectively, for the subject-dependent mode. In addition, we used t-SNE to visualize the features extracted by the proposed network, further demonstrating the effectiveness of the feature extraction framework. We also conducted extensive ablation and hyperparameter tuning experiments to construct a robust network architecture that can be well generalized.
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Affiliation(s)
- Xingbin Shi
- The School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai 201306, China
| | - Baojiang Li
- The School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai 201306, China.
| | - Wenlong Wang
- The School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai 201306, China
| | - Yuxin Qin
- The School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai 201306, China
| | - Haiyan Wang
- The School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai 201306, China
| | - Xichao Wang
- The School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai 201306, China
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Deng Y, Ji Z, Wang Y, Zhou SK. OS-SSVEP: One-shot SSVEP classification. Neural Netw 2024; 180:106734. [PMID: 39332212 DOI: 10.1016/j.neunet.2024.106734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 07/15/2024] [Accepted: 09/10/2024] [Indexed: 09/29/2024]
Abstract
It is extremely challenging to classify steady-state visual evoked potentials (SSVEPs) in scenarios characterized by a huge scarcity of calibration data where only one calibration trial is available for each stimulus target. To address this challenge, we introduce a novel approach named OS-SSVEP, which combines a dual domain cross-subject fusion network (CSDuDoFN) with the task-related and task-discriminant component analysis (TRCA and TDCA) based on data augmentation. The CSDuDoFN framework is designed to comprehensively transfer information from source subjects, while TRCA and TDCA are employed to exploit the information from the single available calibration trial of the target subject. Specifically, CSDuDoFN uses multi-reference least-squares transformation (MLST) to map data from both the source subjects and the target subject into the domain of sine-cosine templates, thereby reducing cross-subject domain gap and benefiting transfer learning. In addition, CSDuDoFN is fed with both transformed and original data, with an adequate fusion of their features occurring at different network layers. To capitalize on the calibration trial of the target subject, OS-SSVEP utilizes source aliasing matrix estimation (SAME)-based data augmentation to incorporate into the training process of the ensemble TRCA (eTRCA) and TDCA models. Ultimately, the outputs of CSDuDoFN, eTRCA, and TDCA are combined for the SSVEP classification. The effectiveness of our proposed approach is comprehensively evaluated on three publicly available SSVEP datasets, achieving the best performance on two datasets and competitive performance on the third. Further, it is worth noting that our method follows a different technical route from the current state-of-the-art (SOTA) method and the two are complementary. The performance is significantly improved when our method is combined with the SOTA method. This study underscores the potential to integrate the SSVEP-based brain-computer interface (BCI) into daily life. The corresponding source code is accessible at https://github.com/Sungden/One-shot-SSVEP-classification.
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Affiliation(s)
- Yang Deng
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, China; Center for Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE), Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215123, China.
| | - Zhiwei Ji
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, 210095, China.
| | - Yijun Wang
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China.
| | - S Kevin Zhou
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, China; Center for Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE), Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215123, China; Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui, 230026, China; Key Laboratory of Intelligent Information Processing of the Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.
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Zhao S, Cao Y, Yang W, Yu J, Xu C, Dai W, Li S, Pan G, Luo B. DOCTer: a novel EEG-based diagnosis framework for disorders of consciousness. J Neural Eng 2024; 21:056021. [PMID: 39255823 DOI: 10.1088/1741-2552/ad7904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2024] [Accepted: 09/10/2024] [Indexed: 09/12/2024]
Abstract
Objective. Accurately diagnosing patients with disorders of consciousness (DOC) is challenging and prone to errors. Recent studies have demonstrated that EEG (electroencephalography), a non-invasive technique of recording the spontaneous electrical activity of brains, offers valuable insights for DOC diagnosis. However, some challenges remain: (1) the EEG signals have not been fully used; and (2) the data scale in most existing studies is limited. In this study, our goal is to differentiate between minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS) using resting-state EEG signals, by proposing a new deep learning framework.Approach. We propose DOCTer, an end-to-end framework for DOC diagnosis based on EEG. It extracts multiple pertinent features from the raw EEG signals, including time-frequency features and microstates. Meanwhile, it takes clinical characteristics of patients into account, and then combines all the features together for the diagnosis. To evaluate its effectiveness, we collect a large-scale dataset containing 409 resting-state EEG recordings from 128 UWS and 187 MCS cases.Main results. Evaluated on our dataset, DOCTer achieves the state-of-the-art performance, compared to other methods. The temporal/spectral features contributes the most to the diagnosis task. The cerebral integrity is important for detecting the consciousness level. Meanwhile, we investigate the influence of different EEG collection duration and number of channels, in order to help make the appropriate choices for clinics.Significance. The DOCTer framework significantly improves the accuracy of DOC diagnosis, helpful for developing appropriate treatment programs. Findings derived from the large-scale dataset provide valuable insights for clinics.
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Affiliation(s)
- Sha Zhao
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
- The State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Yue Cao
- School of Software Technology, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
- The State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Wei Yang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
- The State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Jie Yu
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China
| | - Chuan Xu
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China
| | - Wei Dai
- Stanford University, 450 Jane Stanford Way, Stanford, CA 94305, United States of America
| | - Shijian Li
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
- The State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Gang Pan
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
- The State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
- MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, People's Republic of China
| | - Benyan Luo
- The State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China
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Zhang R, Rong R, Xu Y, Wang H, Wang X. OxcarNet: sinc convolutional network with temporal and channel attention for prediction of oxcarbazepine monotherapy responses in patients with newly diagnosed epilepsy. J Neural Eng 2024; 21:056019. [PMID: 39250934 DOI: 10.1088/1741-2552/ad788c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 09/09/2024] [Indexed: 09/11/2024]
Abstract
Objective.Monotherapy with antiepileptic drugs (AEDs) is the preferred strategy for the initial treatment of epilepsy. However, an inadequate response to the initially prescribed AED is a significant indicator of a poor long-term prognosis, emphasizing the importance of precise prediction of treatment outcomes with the initial AED regimen in patients with epilepsy.Approach. We introduce OxcarNet, an end-to-end neural network framework developed to predict treatment outcomes in patients undergoing oxcarbazepine monotherapy. The proposed predictive model adopts a Sinc Module in its initial layers for adaptive identification of discriminative frequency bands. The derived feature maps are then processed through a Spatial Module, which characterizes the scalp distribution patterns of the electroencephalography (EEG) signals. Subsequently, these features are fed into an attention-enhanced Temporal Module to capture temporal dynamics and discrepancies. A channel module with an attention mechanism is employed to reveal inter-channel dependencies within the output of the Temporal Module, ultimately achieving response prediction. OxcarNet was rigorously evaluated using a proprietary dataset of retrospectively collected EEG data from newly diagnosed epilepsy patients at Nanjing Drum Tower Hospital. This dataset included patients who underwent long-term EEG monitoring in a clinical inpatient setting.Main results.OxcarNet demonstrated exceptional accuracy in predicting treatment outcomes for patients undergoing Oxcarbazepine monotherapy. In the ten-fold cross-validation, the model achieved an accuracy of 97.27%, and in the validation involving unseen patient data, it maintained an accuracy of 89.17%, outperforming six conventional machine learning methods and three generic neural decoding networks. These findings underscore the model's effectiveness in accurately predicting the treatment responses in patients with newly diagnosed epilepsy. The analysis of features extracted by the Sinc filters revealed a predominant concentration of predictive frequencies in the high-frequency range of the gamma band.Significance. The findings of our study offer substantial support and new insights into tailoring early AED selection, enhancing the prediction accuracy for the responses of AEDs.
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Affiliation(s)
- Runkai Zhang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, People's Republic of China
| | - Rong Rong
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing 210008, Jiangsu, People's Republic of China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing 210008, Jiangsu, People's Republic of China
| | - Haixian Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, People's Republic of China
| | - Xiaoyun Wang
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing 210008, Jiangsu, People's Republic of China
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Lee MH, Shomanov A, Begim B, Kabidenova Z, Nyssanbay A, Yazici A, Lee SW. EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts. Sci Data 2024; 11:1026. [PMID: 39300129 PMCID: PMC11413008 DOI: 10.1038/s41597-024-03838-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 08/29/2024] [Indexed: 09/22/2024] Open
Abstract
Understanding emotional states is pivotal for the development of next-generation human-machine interfaces. Human behaviors in social interactions have resulted in psycho-physiological processes influenced by perceptual inputs. Therefore, efforts to comprehend brain functions and human behavior could potentially catalyze the development of AI models with human-like attributes. In this study, we introduce a multimodal emotion dataset comprising data from 30-channel electroencephalography (EEG), audio, and video recordings from 42 participants. Each participant engaged in a cue-based conversation scenario, eliciting five distinct emotions: neutral, anger, happiness, sadness, and calmness. Throughout the experiment, each participant contributed 200 interactions, which encompassed both listening and speaking. This resulted in a cumulative total of 8,400 interactions across all participants. We evaluated the baseline performance of emotion recognition for each modality using established deep neural network (DNN) methods. The Emotion in EEG-Audio-Visual (EAV) dataset represents the first public dataset to incorporate three primary modalities for emotion recognition within a conversational context. We anticipate that this dataset will make significant contributions to the modeling of the human emotional process, encompassing both fundamental neuroscience and machine learning viewpoints.
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Affiliation(s)
- Min-Ho Lee
- Nazarbayev University, Department of Computer Science, Astana, 010000, Republic of Kazakhstan
| | - Adai Shomanov
- Nazarbayev University, Department of Computer Science, Astana, 010000, Republic of Kazakhstan
| | - Balgyn Begim
- Nazarbayev University, Department of Computer Science, Astana, 010000, Republic of Kazakhstan
| | - Zhuldyz Kabidenova
- Nazarbayev University, Department of Computer Science, Astana, 010000, Republic of Kazakhstan
| | - Aruna Nyssanbay
- Nazarbayev University, Department of Computer Science, Astana, 010000, Republic of Kazakhstan
| | - Adnan Yazici
- Nazarbayev University, Department of Computer Science, Astana, 010000, Republic of Kazakhstan
| | - Seong-Whan Lee
- Korea University, Department of Artificial Intelligence, Seoul, 02841, Republic of Korea.
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Zhou P, Deng H, Zeng J, Ran H, Yu C. Unconscious classification of quantitative electroencephalogram features from propofol versus propofol combined with etomidate anesthesia using one-dimensional convolutional neural network. Front Med (Lausanne) 2024; 11:1447951. [PMID: 39359920 PMCID: PMC11445052 DOI: 10.3389/fmed.2024.1447951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Accepted: 09/05/2024] [Indexed: 10/04/2024] Open
Abstract
Objective Establishing a convolutional neural network model for the recognition of characteristic raw electroencephalogram (EEG) signals is crucial for monitoring consciousness levels and guiding anesthetic drug administration. Methods This trial was conducted from December 2023 to March 2024. A total of 40 surgery patients were randomly divided into either a propofol group (1% propofol injection, 10 mL: 100 mg) (P group) or a propofol-etomidate combination group (1% propofol injection, 10 mL: 100 mg, and 0.2% etomidate injection, 10 mL: 20 mg, mixed at a 2:1 volume ratio) (EP group). In the P group, target-controlled infusion (TCI) was employed for sedation induction, with an initial effect site concentration set at 5-6 μg/mL. The EP group received an intravenous push with a dosage of 0.2 mL/kg. Six consciousness-related EEG features were extracted from both groups and analyzed using four prediction models: support vector machine (SVM), Gaussian Naive Bayes (GNB), artificial neural network (ANN), and one-dimensional convolutional neural network (1D CNN). The performance of the models was evaluated based on accuracy, precision, recall, and F1-score. Results The power spectral density (94%) and alpha/beta ratio (72%) demonstrated higher accuracy as indicators for assessing consciousness. The classification accuracy of the 1D CNN model for anesthesia-induced unconsciousness (97%) surpassed that of the SVM (83%), GNB (81%), and ANN (83%) models, with a significance level of p < 0.05. Furthermore, the mean and mean difference ± standard error of the primary power values for the EP and P groups during the induced period were as follows: delta (23.85 and 16.79, 7.055 ± 0.817, p < 0.001), theta (10.74 and 8.743, 1.995 ± 0.7045, p < 0.02), and total power (24.31 and 19.72, 4.588 ± 0.7107, p < 0.001). Conclusion Large slow-wave oscillations, power spectral density, and the alpha/beta ratio are effective indicators of changes in consciousness during intravenous anesthesia with a propofol-etomidate combination. These indicators can aid anesthesiologists in evaluating the depth of anesthesia and adjusting dosages accordingly. The 1D CNN model, which incorporates consciousness-related EEG features, represents a promising tool for assessing the depth of anesthesia. Clinical Trial Registration https://www.chictr.org.cn/index.html.
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Affiliation(s)
- Pan Zhou
- Department of Anesthesiology, Stomatological Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China
| | - Haixia Deng
- Department of Anesthesiology, Stomatological Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China
| | - Jie Zeng
- Department of Anesthesiology, Stomatological Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China
| | - Haosong Ran
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing, China
| | - Cong Yu
- Department of Anesthesiology, Stomatological Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China
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Rong F, Yang B, Guan C. Decoding Multi-Class Motor Imagery From Unilateral Limbs Using EEG Signals. IEEE Trans Neural Syst Rehabil Eng 2024; 32:3399-3409. [PMID: 39236133 DOI: 10.1109/tnsre.2024.3454088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2024]
Abstract
The EEG is a widely utilized neural signal source, particularly in motor imagery-based brain-computer interface (MI-BCI), offering distinct advantages in applications like stroke rehabilitation. Current research predominantly concentrates on the bilateral limbs paradigm and decoding, but the use scenarios for stroke rehabilitation are typically for unilateral upper limbs. There is a significant challenge to decoding unilateral MI of multitasks due to the overlapped spatial neural activities of the tasks. This study aims to formulate a novel MI-BCI experimental paradigm for unilateral limbs with multitasks. The paradigm encompasses four imagined movement directions: top-bottom, left-right, top right-bottom left, and top left-bottom right. Forty-six healthy subjects participated in this experiment. Commonly used machine learning techniques, such as FBCSP, EEGNet, deepConvNet, and FBCNet, were employed for evaluation. To improve decoding accuracy, we propose an MVCA method that introduces temporal convolution and attention mechanism to effectively capture temporal features from multiple perspectives. With the MVCA model, we have achieved 40.6% and 64.89% classification accuracies for the four-class and two-class scenarios (top right-bottom left and top left-bottom right), respectively. Conclusion: This is the first study demonstrating that motor imagery of multiple directions in unilateral limbs can be decoded. In particular, decoding two directions, right top to left bottom and left top to right bottom, provides the best accuracy, which sheds light on future studies. This study advances the development of the MI-BCI paradigm, offering preliminary evidence for the feasibility of decoding multiple directional information from EEG. This, in turn, enhances the dimensions of MI control commands.
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Sung DJ, Kim KT, Jeong JH, Kim L, Lee SJ, Kim H, Kim SJ. Improving inter-session performance via relevant session-transfer for multi-session motor imagery classification. Heliyon 2024; 10:e37343. [PMID: 39296025 PMCID: PMC11409124 DOI: 10.1016/j.heliyon.2024.e37343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2024] [Accepted: 09/02/2024] [Indexed: 09/21/2024] Open
Abstract
Motor imagery (MI)-based brain-computer interfaces (BCIs) using electroencephalography (EEG) have found practical applications in external device control. However, the non-stationary nature of EEG signals remains to obstruct BCI performance across multiple sessions, even for the same user. In this study, we aim to address the impact of non-stationarity, also known as inter-session variability, on multi-session MI classification performance by introducing a novel approach, the relevant session-transfer (RST) method. Leveraging the cosine similarity as a benchmark, the RST method transfers relevant EEG data from the previous session to the current one. The effectiveness of the proposed RST method was investigated through performance comparisons with the self-calibrating method, which uses only the data from the current session, and the whole-session transfer method, which utilizes data from all prior sessions. We validated the effectiveness of these methods using two datasets: a large MI public dataset (Shu Dataset) and our own dataset of gait-related MI, which includes both healthy participants and individuals with spinal cord injuries. Our experimental results revealed that the proposed RST method leads to a 2.29 % improvement (p < 0.001) in the Shu Dataset and up to a 6.37 % improvement in our dataset when compared to the self-calibrating method. Moreover, our method surpassed the performance of the recent highest-performing method that utilized the Shu Dataset, providing further support for the efficacy of the RST method in improving multi-session MI classification performance. Consequently, our findings confirm that the proposed RST method can improve classification performance across multiple sessions in practical MI-BCIs.
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Affiliation(s)
- Dong-Jin Sung
- Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
- Department of Biomedical Engineering, Korea University College of Medicine, Seoul, 02841, Republic of Korea
| | - Keun-Tae Kim
- Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
- College of Information Science, Hallym University, Chuncheon, 24252, Republic of Korea
| | - Ji-Hyeok Jeong
- Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Laehyun Kim
- Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
| | - Song Joo Lee
- Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
- Division of Bio-Medical Science and Technology, KIST School, Korea University of Science and Technology, Seoul, 02792, Republic of Korea
| | - Hyungmin Kim
- Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
- Division of Bio-Medical Science and Technology, KIST School, Korea University of Science and Technology, Seoul, 02792, Republic of Korea
| | - Seung-Jong Kim
- Department of Biomedical Engineering, Korea University College of Medicine, Seoul, 02841, Republic of Korea
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23
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Tan X, Wang D, Xu M, Chen J, Wu S. Efficient Multi-View Graph Convolutional Network with Self-Attention for Multi-Class Motor Imagery Decoding. Bioengineering (Basel) 2024; 11:926. [PMID: 39329668 PMCID: PMC11428916 DOI: 10.3390/bioengineering11090926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2024] [Revised: 09/11/2024] [Accepted: 09/14/2024] [Indexed: 09/28/2024] Open
Abstract
Research on electroencephalogram-based motor imagery (MI-EEG) can identify the limbs of subjects that generate motor imagination by decoding EEG signals, which is an important issue in the field of brain-computer interface (BCI). Existing deep-learning-based classification methods have not been able to entirely employ the topological information among brain regions, and thus, the classification performance needs further improving. In this paper, we propose a multi-view graph convolutional attention network (MGCANet) with residual learning structure for multi-class MI decoding. Specifically, we design a multi-view graph convolution spatial feature extraction method based on the topological relationship of brain regions to achieve more comprehensive information aggregation. During the modeling, we build an adaptive weight fusion (Awf) module to adaptively merge feature from different brain views to improve classification accuracy. In addition, the self-attention mechanism is introduced for feature selection to expand the receptive field of EEG signals to global dependence and enhance the expression of important features. The proposed model is experimentally evaluated on two public MI datasets and achieved a mean accuracy of 78.26% (BCIC IV 2a dataset) and 73.68% (OpenBMI dataset), which significantly outperforms representative comparative methods in classification accuracy. Comprehensive experiment results verify the effectiveness of our proposed method, which can provide novel perspectives for MI decoding.
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Affiliation(s)
| | - Dan Wang
- College of Computer Science, Beijing University of Technology, Beijing 100124, China; (X.T.)
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24
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Huang J, Chang Y, Li W, Tong J, Du S. A Spatio-Temporal Capsule Neural Network with Self-Correlation Routing for EEG Decoding of Semantic Concepts of Imagination and Perception Tasks. SENSORS (BASEL, SWITZERLAND) 2024; 24:5988. [PMID: 39338733 PMCID: PMC11436183 DOI: 10.3390/s24185988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Revised: 09/05/2024] [Accepted: 09/12/2024] [Indexed: 09/30/2024]
Abstract
Decoding semantic concepts for imagination and perception tasks (SCIP) is important for rehabilitation medicine as well as cognitive neuroscience. Electroencephalogram (EEG) is commonly used in the relevant fields, because it is a low-cost noninvasive technique with high temporal resolution. However, as EEG signals contain a high noise level resulting in a low signal-to-noise ratio, it makes decoding EEG-based semantic concepts for imagination and perception tasks (SCIP-EEG) challenging. Currently, neural network algorithms such as CNN, RNN, and LSTM have almost reached their limits in EEG signal decoding due to their own short-comings. The emergence of transformer methods has improved the classification performance of neural networks for EEG signals. However, the transformer model has a large parameter set and high complexity, which is not conducive to the application of BCI. EEG signals have high spatial correlation. The relationship between signals from different electrodes is more complex. Capsule neural networks can effectively model the spatial relationship between electrodes through vector representation and a dynamic routing mechanism. Therefore, it achieves more accurate feature extraction and classification. This paper proposes a spatio-temporal capsule network with a self-correlation routing mechaninsm for the classification of semantic conceptual EEG signals. By improving the feature extraction and routing mechanism, the model is able to more effectively capture the highly variable spatio-temporal features from EEG signals and establish connections between capsules, thereby enhancing classification accuracy and model efficiency. The performance of the proposed model was validated using the publicly accessible semantic concept dataset for imagined and perceived tasks from Bath University. Our model achieved average accuracies of 94.9%, 93.3%, and 78.4% in the three sensory modalities (pictorial, orthographic, and audio), respectively. The overall average accuracy across the three sensory modalities is 88.9%. Compared to existing advanced algorithms, the proposed model achieved state-of-the-art performance, significantly improving classification accuracy. Additionally, the proposed model is more stable and efficient, making it a better decoding solution for SCIP-EEG decoding.
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Affiliation(s)
- Jianxi Huang
- School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China
| | - Yinghui Chang
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin 300381, China
| | - Wenyu Li
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Jigang Tong
- School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China
| | - Shengzhi Du
- Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa
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25
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Pulferer HS, Kostoglou K, Müller-Putz GR. Improving non-invasive trajectory decoding via neural correlates of continuous erroneous feedback processing. J Neural Eng 2024; 21:056010. [PMID: 39231465 DOI: 10.1088/1741-2552/ad7762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Accepted: 09/04/2024] [Indexed: 09/06/2024]
Abstract
Objective. Over the last decades, error-related potentials (ErrPs) have repeatedly proven especially useful as corrective mechanisms in invasive and non-invasive brain-computer interfaces (BCIs). However, research in this context exclusively investigated the distinction of discrete events intocorrectorerroneousto the present day. Due to this predominant formulation as a binary classification problem, classical ErrP-based BCIs fail to monitor tasks demanding quantitative information on error severity rather than mere qualitative decisions on error occurrence. As a result, fine-tuned and natural feedback control based on continuously perceived deviations from an intended target remains beyond the capabilities of previously used BCI setups.Approach.To address this issue for future BCI designs, we investigated the feasibility of regressing rather than classifying error-related activity non-invasively from the brain.Main results.Using pre-recorded data from ten able-bodied participants in three sessions each and a multi-output convolutional neural network, we demonstrated the above-chance regression of ongoing target-feedback discrepancies from brain signals in a pseudo-online fashion. In a second step, we used this inferred information about the target deviation to correct the initially displayed feedback accordingly, reporting significant improvements in correlations between corrected feedback and target trajectories across feedback conditions.Significance.Our results indicate that continuous information on target-feedback discrepancies can be successfully regressed from cortical activity, paving the way to increasingly naturalistic, fine-tuned correction mechanisms for future BCI applications.
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Affiliation(s)
- Hannah S Pulferer
- Institute of Neural Engineering, TU Graz, Stremayrgasse 16/4, Graz, 8010 Styria, Austria
| | - Kyriaki Kostoglou
- Institute of Neural Engineering, TU Graz, Stremayrgasse 16/4, Graz, 8010 Styria, Austria
| | - Gernot R Müller-Putz
- Institute of Neural Engineering, TU Graz, Stremayrgasse 16/4, Graz, 8010 Styria, Austria
- BioTechMed-Graz, Graz, Styria, Austria
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26
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Borra D, Paissan F, Ravanelli M. SpeechBrain-MOABB: An open-source Python library for benchmarking deep neural networks applied to EEG signals. Comput Biol Med 2024; 182:109097. [PMID: 39265481 DOI: 10.1016/j.compbiomed.2024.109097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 08/20/2024] [Accepted: 08/30/2024] [Indexed: 09/14/2024]
Abstract
Deep learning has revolutionized EEG decoding, showcasing its ability to outperform traditional machine learning models. However, unlike other fields, EEG decoding lacks comprehensive open-source libraries dedicated to neural networks. Existing tools (MOABB and braindecode) prevent the creation of robust and complete decoding pipelines, as they lack support for hyperparameter search across the entire pipeline, and are sensitive to fluctuations in results due to network random initialization. Furthermore, the absence of a standardized experimental protocol exacerbates the reproducibility crisis in the field. To address these limitations, we introduce SpeechBrain-MOABB, a novel open-source toolkit carefully designed to facilitate the development of a comprehensive EEG decoding pipeline based on deep learning. SpeechBrain-MOABB incorporates a complete experimental protocol that standardizes critical phases, such as hyperparameter search and model evaluation. It natively supports multi-step hyperparameter search for finding the optimal hyperparameters in a high-dimensional space defined by the entire pipeline, and multi-seed training and evaluation for obtaining performance estimates robust to the variability caused by random initialization. SpeechBrain-MOABB outperforms other libraries, including MOABB and braindecode, with accuracy improvements of 14.9% and 25.2% (on average), respectively. By enabling easy-to-use and easy-to-share decoding pipelines, our toolkit can be exploited by neuroscientists for decoding EEG with neural networks in a replicable and trustworthy way.
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Affiliation(s)
- Davide Borra
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Cesena, Forlì-Cesena, Italy.
| | | | - Mirco Ravanelli
- Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada; Mila - Quebec AI Institute, Montreal, Quebec, Canada
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27
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Tian X, Zhu L, Zhang M, Wang S, Lu Y, Xu X, Jia W, Zheng Y, Song S. Social anxiety prediction based on ERP features: A deep learning approach. J Affect Disord 2024; 367:545-553. [PMID: 39236887 DOI: 10.1016/j.jad.2024.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 08/24/2024] [Accepted: 09/02/2024] [Indexed: 09/07/2024]
Abstract
BACKGROUND Social Anxiety Disorder is traditionally diagnosed using subjective scales that may lack accuracy. Recently, EEG technology has gained importance for anxiety detection due to its ability to capture stable and objective neurophysiological activities. However, existing methods mainly focus on extracting EEG features during resting states, with limited use of psychologically features like Event-Related Potential (ERP) in task-related states for anxiety detection in deep learning frameworks. METHODS We collected EEG data from 63 participants exposed to four facial expressions and extracted task-relevant features. Using the EEGNet model, we predicted social anxiety and evaluated its performance using metrics such as accuracy, F1 score, sensitivity, and specificity. We compared EEGNet's performance with Deep Convolutional Neural Network (DeepConvNet), ShallowConvNet, Bi-directional Long Short-Term Memory (BiLSTM), and SVM. To assess the generalizability of the results, we carried out the same procedure on our prior dataset. RESULTS EEGNet outperformed other models, achieving 99.16 % accuracy with Late Positive Potential (LPP). ERP components yielded higher accuracy than time-domain and frequency-domain features for social anxiety recognition. Accuracy was better for neutral and negative facial stimuli. Consistency across two datasets indicates stability of findings. LIMITATIONS Due to limited publicly available task-state datasets, only our own were used. Future studies could assess generalizability on larger datasets from different sources. CONCLUSIONS We conducted the first test of ERP features in anxiety recognition tasks. Results show ERP features have greater potential in social anxiety recognition, with LPP exhibiting high stability and accuracy. Outcomes indicate recognizing social anxiety with negative or neutral facial stimuli is more useful.
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Affiliation(s)
- Xiaodong Tian
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Lingkai Zhu
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Mingxian Zhang
- School of Education and Psychology, University of Jinan, Jinan, China
| | - Songling Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yi Lu
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Xiaolei Xu
- School of Psychology, Shandong Normal University, Jinan, China
| | - Weikuan Jia
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Sutao Song
- School of Information Science and Engineering, Shandong Normal University, Jinan, China; School of Education and Psychology, University of Jinan, Jinan, China.
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28
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Kumari A, Edla DR, Reddy RR, Jannu S, Vidyarthi A, Alkhayyat A, de Marin MSG. EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning. J Neurosci Methods 2024; 409:110215. [PMID: 38968976 DOI: 10.1016/j.jneumeth.2024.110215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 06/14/2024] [Accepted: 06/28/2024] [Indexed: 07/07/2024]
Abstract
Brain-computer interface (BCI) technology holds promise for individuals with profound motor impairments, offering the potential for communication and control. Motor imagery (MI)-based BCI systems are particularly relevant in this context. Despite their potential, achieving accurate and robust classification of MI tasks using electroencephalography (EEG) data remains a significant challenge. In this paper, we employed the Minimum Redundancy Maximum Relevance (MRMR) algorithm to optimize channel selection. Furthermore, we introduced a hybrid optimization approach that combines the War Strategy Optimization (WSO) and Chimp Optimization Algorithm (ChOA). This hybridization significantly enhances the classification model's overall performance and adaptability. A two-tier deep learning architecture is proposed for classification, consisting of a Convolutional Neural Network (CNN) and a modified Deep Neural Network (M-DNN). The CNN focuses on capturing temporal correlations within EEG data, while the M-DNN is designed to extract high-level spatial characteristics from selected EEG channels. Integrating optimal channel selection, hybrid optimization, and the two-tier deep learning methodology in our BCI framework presents an enhanced approach for precise and effective BCI control. Our model got 95.06% accuracy with high precision. This advancement has the potential to significantly impact neurorehabilitation and assistive technology applications, facilitating improved communication and control for individuals with motor impairments.
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Affiliation(s)
- Annu Kumari
- Department of Computer Science and Engineering, National Institute of Technology Goa, Cuncolim, South Goa, 403 703, Goa, India.
| | - Damodar Reddy Edla
- Department of Computer Science and Engineering, National Institute of Technology Goa, Cuncolim, South Goa, 403 703, Goa, India.
| | - R Ravinder Reddy
- Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad, 500 075, India.
| | - Srikanth Jannu
- Department of Computer Science and Engineering, Vaagdevi Engineering College, Warangal, Telangana, 506 005, India.
| | - Ankit Vidyarthi
- Department of CSE&IT, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, 201309, India.
| | | | - Mirtha Silvana Garat de Marin
- Engineering Research & Innovation Group, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain; Department of Project Management, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA; Department of Project Management, Universidade Internacional do Cuanza, Estrada Nacional 250, Bairro Kaluapanda, Cuito-Bié, Angola.
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29
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Chen C, Song Y, Chen D, Zhu J, Ning H, Xiao R. Design and application of pneumatic rehabilitation glove system based on brain-computer interface. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2024; 95:095108. [PMID: 39248624 DOI: 10.1063/5.0225972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Accepted: 08/22/2024] [Indexed: 09/10/2024]
Abstract
Stroke has been the second leading cause of death and disability worldwide. With the innovation of therapeutic schedules, its death rate has decreased significantly but still guides chronic movement disorders. Due to the lack of independent activities and minimum exercise standards, the traditional rehabilitation means of occupational therapy and constraint-induced movement therapy pose challenges in stroke patients with severe impairments. Therefore, specific and effective rehabilitation methods seek innovation. To address the overlooked limitation, we design a pneumatic rehabilitation glove system. Specially, we developed a pneumatic glove, which utilizes ElectroEncephaloGram (EEG) acquisition to gain the EEG signals. A proposed EEGTran model is inserted into the system to distinguish the specific motor imagination behavior, thus, the glove can perform specific activities according to the patient's imagination, facilitating the patients with severe movement disorders and promoting the rehabilitation technology. The experimental results show that the proposed EEGTrans reached an accuracy of 87.3% and outperformed that of competitors. It demonstrates that our pneumatic rehabilitation glove system contributes to the rehabilitation training of stroke patients.
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Affiliation(s)
- Cheng Chen
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Yize Song
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Duoyou Chen
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Jiahua Zhu
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Huansheng Ning
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Ruoxiu Xiao
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Shunde Graduate School of University of Science and Technology Beijing, Foshan 100024, China
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30
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Zhao W, Jiang X, Zhang B, Xiao S, Weng S. CTNet: a convolutional transformer network for EEG-based motor imagery classification. Sci Rep 2024; 14:20237. [PMID: 39215126 PMCID: PMC11364810 DOI: 10.1038/s41598-024-71118-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 08/26/2024] [Indexed: 09/04/2024] Open
Abstract
Brain-computer interface (BCI) technology bridges the direct communication between the brain and machines, unlocking new possibilities for human interaction and rehabilitation. EEG-based motor imagery (MI) plays a pivotal role in BCI, enabling the translation of thought into actionable commands for interactive and assistive technologies. However, the constrained decoding performance of brain signals poses a limitation to the broader application and development of BCI systems. In this study, we introduce a convolutional Transformer network (CTNet) designed for EEG-based MI classification. Firstly, CTNet employs a convolutional module analogous to EEGNet, dedicated to extracting local and spatial features from EEG time series. Subsequently, it incorporates a Transformer encoder module, leveraging a multi-head attention mechanism to discern the global dependencies of EEG's high-level features. Finally, a straightforward classifier module comprising fully connected layers is followed to categorize EEG signals. In subject-specific evaluations, CTNet achieved remarkable decoding accuracies of 82.52% and 88.49% on the BCI IV-2a and IV-2b datasets, respectively. Furthermore, in the challenging cross-subject assessments, CTNet achieved recognition accuracies of 58.64% on the BCI IV-2a dataset and 76.27% on the BCI IV-2b dataset. In both subject-specific and cross-subject evaluations, CTNet holds a leading position when compared to some of the state-of-the-art methods. This underscores the exceptional efficacy of our approach and its potential to set a new benchmark in EEG decoding.
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Affiliation(s)
- Wei Zhao
- Chengyi College, Jimei University, Xiamen, 361021, China.
| | - Xiaolu Jiang
- Chengyi College, Jimei University, Xiamen, 361021, China
| | - Baocan Zhang
- Chengyi College, Jimei University, Xiamen, 361021, China
| | - Shixiao Xiao
- Chengyi College, Jimei University, Xiamen, 361021, China
| | - Sujun Weng
- Chengyi College, Jimei University, Xiamen, 361021, China
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31
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Choo S, Park H, Jung JY, Flores K, Nam CS. Improving classification performance of motor imagery BCI through EEG data augmentation with conditional generative adversarial networks. Neural Netw 2024; 180:106665. [PMID: 39241437 DOI: 10.1016/j.neunet.2024.106665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 08/12/2024] [Accepted: 08/23/2024] [Indexed: 09/09/2024]
Abstract
In brain-computer interface (BCI), building accurate electroencephalogram (EEG) classifiers for specific mental tasks is critical for BCI performance. The classifiers are developed by machine learning (ML) and deep learning (DL) techniques, requiring a large dataset for training to build reliable and accurate models. However, collecting large enough EEG datasets is difficult due to intra-/inter-subject variabilities and experimental costs. This leads to the data scarcity problem, which causes overfitting issues to training samples, resulting in reducing generalization performance. To solve the EEG data scarcity problem and improve the performance of the EEG classifiers, we propose a novel EEG data augmentation (DA) framework using conditional generative adversarial networks (cGANs). An experimental study is implemented with two public EEG datasets, including motor imagery (MI) tasks (BCI competition IV IIa and III IVa), to validate the effectiveness of the proposed EEG DA method for the EEG classifiers. To evaluate the proposed cGAN-based DA method, we tested eight EEG classifiers for the experiment, including traditional MLs and state-of-the-art DLs with three existing EEG DA methods. Experimental results showed that most DA methods with proper DA proportion in the training dataset had higher classification performances than without DA. Moreover, applying the proposed DA method showed superior classification performance improvement than the other DA methods. This shows that the proposed method is a promising EEG DA method for enhancing the performances of the EEG classifiers in MI-based BCIs.
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Affiliation(s)
- Sanghyun Choo
- Department of Industrial Engineering, Kumoh National Institute of Technology, South Korea
| | - Hoonseok Park
- Department of Big Data Analytics, Kyung Hee University, South Korea
| | - Jae-Yoon Jung
- Department of Big Data Analytics, Kyung Hee University, South Korea; Department of Industrial and Management Systems Engineering, Kyung Hee University, South Korea
| | - Kevin Flores
- Department of Mathematics, North Carolina State University, Raleigh, NC, USA
| | - Chang S Nam
- Department of Industrial and Management Systems Engineering, Kyung Hee University, South Korea; Department of Industrial and Systems Engineering, Northern Illinois University, DeKalb, IL, USA.
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32
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Delgado-Munoz J, Matsunaka R, Hiraki K. Classification of Known and Unknown Study Items in a Memory Task Using Single-Trial Event-Related Potentials and Convolutional Neural Networks. Brain Sci 2024; 14:860. [PMID: 39335356 PMCID: PMC11430714 DOI: 10.3390/brainsci14090860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2024] [Revised: 08/18/2024] [Accepted: 08/25/2024] [Indexed: 09/30/2024] Open
Abstract
This study examines the feasibility of using event-related potentials (ERPs) obtained from electroencephalographic (EEG) recordings as biomarkers for long-term memory item classification. Previous studies have identified old/new effects in memory paradigms associated with explicit long-term memory and familiarity. Recent advancements in convolutional neural networks (CNNs) have enabled the classification of ERP trials under different conditions and the identification of features related to neural processes at the single-trial level. We employed this approach to compare three CNN models with distinct architectures using experimental data. Participants (N = 25) performed an association memory task while recording ERPs that were used for training and validation of the CNN models. The EEGNET-based model achieved the most reliable performance in terms of precision, recall, and specificity compared with the shallow and deep convolutional approaches. The classification accuracy of this model reached 62% for known items and 66% for unknown items. Good overall accuracy requires a trade-off between recall and specificity and depends on the architecture of the model and the dataset size. These results suggest the possibility of integrating ERP and CNN into online learning tools and identifying the underlying processes related to long-term memorization.
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Affiliation(s)
- Jorge Delgado-Munoz
- Graduate School of Arts and Sciences, The University of Tokyo, Meguro-Ku, Tokyo 153-8902, Japan; (R.M.); (K.H.)
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33
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Alsuradi H, Shen J, Park W, Eid M. Evaluation of perceived urgency from single-trial EEG data elicited by upper-body vibration feedback using deep learning. Sci Rep 2024; 14:19604. [PMID: 39179642 PMCID: PMC11344029 DOI: 10.1038/s41598-024-70508-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Accepted: 08/19/2024] [Indexed: 08/26/2024] Open
Abstract
Notification systems that convey urgency without adding cognitive burden are crucial in human-computer interaction. Haptic feedback systems, particularly those utilizing vibration feedback, have emerged as a compelling solution, capable of providing desirable levels of urgency depending on the application. High-risk applications require an evaluation of the urgency level elicited during critical notifications. Traditional evaluations of perceived urgency rely on subjective self-reporting and performance metrics, which, while useful, are not real-time and can be distracting from the task at hand. In contrast, EEG technology offers a direct, non-intrusive method of assessing the user's cognitive state. Leveraging deep learning, this study introduces a novel approach to evaluate perceived urgency from single-trial EEG data, induced by vibration stimuli on the upper body, utilizing our newly collected urgency-via-vibration dataset. The proposed model combines a 2D convolutional neural network with a temporal convolutional network to capture spatial and temporal EEG features, outperforming several established EEG models. The proposed model achieves an average classification accuracy of 83% through leave-one-subject-out cross-validation across three urgency classes (not urgent, urgent, and very urgent) from a single trial of EEG data. Furthermore, explainability analysis showed that the prefrontal brain region, followed by the central brain region, are the most influential in predicting the urgency level. A follow-up neural statistical analysis revealed an increase in event-related synchronization (ERS) in the theta frequency band (4-7 Hz) with the increased level of urgency, which is associated with high arousal and attention in the neuroscience literature. A limitation of this study is that the proposed model's performance was tested only the urgency-via-vibration dataset, which may affect the generalizability of the findings.
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Affiliation(s)
- Haneen Alsuradi
- Engineering Division, New York University Abu Dhabi, Abu Dhabi, UAE
- Center for Artificial Intelligence and Robotics, New York University Abu Dhabi, Abu Dhabi, UAE
| | - Jiacheng Shen
- Computer Science Department, New York University Shanghai, Shanghai, China
| | - Wanjoo Park
- Engineering Division, New York University Abu Dhabi, Abu Dhabi, UAE
| | - Mohamad Eid
- Engineering Division, New York University Abu Dhabi, Abu Dhabi, UAE.
- Center for Artificial Intelligence and Robotics, New York University Abu Dhabi, Abu Dhabi, UAE.
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34
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Moraes CPA, Dos Santos LH, Fantinato DG, Neves A, Adali T. Independent Vector Analysis for Feature Extraction in Motor Imagery Classification. SENSORS (BASEL, SWITZERLAND) 2024; 24:5428. [PMID: 39205122 PMCID: PMC11359939 DOI: 10.3390/s24165428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 08/19/2024] [Accepted: 08/20/2024] [Indexed: 09/04/2024]
Abstract
Independent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statistical dependency between different datasets through mutual information. In the context of motor imagery classification based on electroencephalogram (EEG) signals for the brain-computer interface (BCI), several methods have been proposed to extract features efficiently, mainly based on common spatial patterns, filter banks, and deep learning. However, most methods use only one dataset at a time, which may not be sufficient for dealing with a multi-source retrieving problem in certain scenarios. From this perspective, this paper proposes an original approach for feature extraction through multiple datasets based on IVA to improve the classification of EEG-based motor imagery movements. The IVA components were used as features to classify imagined movements using consolidated classifiers (support vector machines and K-nearest neighbors) and deep classifiers (EEGNet and EEGInception). The results show an interesting performance concerning the clustering of MI-based BCI patients, and the proposed method reached an average accuracy of 86.7%.
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Affiliation(s)
- Caroline Pires Alavez Moraes
- Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Santo André 09280-560, SP, Brazil
| | - Lucas Heck Dos Santos
- Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Santo André 09280-560, SP, Brazil
| | - Denis Gustavo Fantinato
- Department of Computer Engineering and Automation (DCA), Universidade Estadual de Campinas (UNICAMP), Campinas 13083-852, SP, Brazil
| | - Aline Neves
- Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Santo André 09280-560, SP, Brazil
| | - Tülay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County (UMBC), Baltimore, MD 21250, USA
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Li S, Daly I, Guan C, Cichocki A, Jin J. Inter-participant transfer learning with attention based domain adversarial training for P300 detection. Neural Netw 2024; 180:106655. [PMID: 39226850 DOI: 10.1016/j.neunet.2024.106655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 06/28/2024] [Accepted: 08/19/2024] [Indexed: 09/05/2024]
Abstract
A Brain-computer interface (BCI) system establishes a novel communication channel between the human brain and a computer. Most event related potential-based BCI applications make use of decoding models, which requires training. This training process is often time-consuming and inconvenient for new users. In recent years, deep learning models, especially participant-independent models, have garnered significant attention in the domain of ERP classification. However, individual differences in EEG signals hamper model generalization, as the ERP component and other aspects of the EEG signal vary across participants, even when they are exposed to the same stimuli. This paper proposes a novel One-source domain transfer learning method based Attention Domain Adversarial Neural Network (OADANN) to mitigate data distribution discrepancies for cross-participant classification tasks. We train and validate our proposed model on both a publicly available OpenBMI dataset and a Self-collected dataset, employing a leave one participant out cross validation scheme. Experimental results demonstrate that the proposed OADANN method achieves the highest and most robust classification performance and exhibits significant improvements when compared to baseline methods (CNN, EEGNet, ShallowNet, DeepCovNet) and domain generalization methods (ERM, Mixup, and Groupdro). These findings underscore the efficacy of our proposed method.
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Affiliation(s)
- Shurui Li
- Center of Intelligent Computing, School of Mathematics, East China University of Science and Technology, Shanghai 200237, China
| | - Ian Daly
- Brain-Computer Interfacing and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, United Kingdom
| | - Cuntai Guan
- School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore
| | - Andrzej Cichocki
- Systems Research Institute, Polish Academy of Science, Warsaw 01-447, Poland; RIKEN Advanced Intelligence Project, Tokyo 103-0027, Japan; Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan
| | - Jing Jin
- Center of Intelligent Computing, School of Mathematics, East China University of Science and Technology, Shanghai 200237, China; Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China.
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36
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Eder M, Xu J, Grosse-Wentrup M. Benchmarking brain-computer interface algorithms: Riemannian approaches vs convolutional neural networks. J Neural Eng 2024; 21:044002. [PMID: 39053485 DOI: 10.1088/1741-2552/ad6793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 07/19/2024] [Indexed: 07/27/2024]
Abstract
Objective.To date, a comprehensive comparison of Riemannian decoding methods with deep convolutional neural networks for EEG-based brain-computer interfaces remains absent from published work. We address this research gap by using MOABB, The Mother Of All BCI Benchmarks, to compare novel convolutional neural networks to state-of-the-art Riemannian approaches across a broad range of EEG datasets, including motor imagery, P300, and steady-state visual evoked potentials paradigms.Approach.We systematically evaluated the performance of convolutional neural networks, specifically EEGNet, shallow ConvNet, and deep ConvNet, against well-established Riemannian decoding methods using MOABB processing pipelines. This evaluation included within-session, cross-session, and cross-subject methods, to provide a practical analysis of model effectiveness and to find an overall solution that performs well across different experimental settings.Main results.We find no significant differences in decoding performance between convolutional neural networks and Riemannian methods for within-session, cross-session, and cross-subject analyses.Significance.The results show that, when using traditional Brain-Computer Interface paradigms, the choice between CNNs and Riemannian methods may not heavily impact decoding performances in many experimental settings. These findings provide researchers with flexibility in choosing decoding approaches based on factors such as ease of implementation, computational efficiency or individual preferences.
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Affiliation(s)
- Manuel Eder
- Research Group Neuroinformatics, Faculty of Computer Science, University of Vienna, Vienna, Austria
| | - Jiachen Xu
- Research Group Neuroinformatics, Faculty of Computer Science, University of Vienna, Vienna, Austria
| | - Moritz Grosse-Wentrup
- Research Group Neuroinformatics, Faculty of Computer Science, University of Vienna, Vienna, Austria
- Data Science Network @ Uni Vienna, University of Vienna, Vienna, Austria
- Vienna Cognitive Science Hub, University of Vienna, Vienna, Austria
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Mattei E, Lozzi D, Di Matteo A, Cipriani A, Manes C, Placidi G. MOVING: A Multi-Modal Dataset of EEG Signals and Virtual Glove Hand Tracking. SENSORS (BASEL, SWITZERLAND) 2024; 24:5207. [PMID: 39204903 PMCID: PMC11359383 DOI: 10.3390/s24165207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 08/01/2024] [Accepted: 08/08/2024] [Indexed: 09/04/2024]
Abstract
Brain-computer interfaces (BCIs) are pivotal in translating neural activities into control commands for external assistive devices. Non-invasive techniques like electroencephalography (EEG) offer a balance of sensitivity and spatial-temporal resolution for capturing brain signals associated with motor activities. This work introduces MOVING, a Multi-Modal dataset of EEG signals and Virtual Glove Hand Tracking. This dataset comprises neural EEG signals and kinematic data associated with three hand movements-open/close, finger tapping, and wrist rotation-along with a rest period. The dataset, obtained from 11 subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. The use of these two devices allows for fast assembly (∼1 min), although introducing more noise than the gold standard devices for data acquisition. The study investigates which frequency bands in EEG signals are the most informative for motor task classification and the impact of baseline reduction on gesture recognition. Deep learning techniques, particularly EEGnetV4, are applied to analyze and classify movements based on the EEG data. This dataset aims to facilitate advances in BCI research and in the development of assistive devices for people with impaired hand mobility. This study contributes to the repository of EEG datasets, which is continuously increasing with data from other subjects, which is hoped to serve as benchmarks for new BCI approaches and applications.
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Affiliation(s)
- Enrico Mattei
- A2VI-Lab, Department of Life, Health and Environmental Sciences, University of L'Aquila, 67100 L'Aquila, Italy
- Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, 67100 L'Aquila, Italy
| | - Daniele Lozzi
- A2VI-Lab, Department of Life, Health and Environmental Sciences, University of L'Aquila, 67100 L'Aquila, Italy
- Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, 67100 L'Aquila, Italy
| | - Alessandro Di Matteo
- A2VI-Lab, Department of Life, Health and Environmental Sciences, University of L'Aquila, 67100 L'Aquila, Italy
- Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, 67100 L'Aquila, Italy
| | - Alessia Cipriani
- A2VI-Lab, Department of Life, Health and Environmental Sciences, University of L'Aquila, 67100 L'Aquila, Italy
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Costanzo Manes
- Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, 67100 L'Aquila, Italy
| | - Giuseppe Placidi
- A2VI-Lab, Department of Life, Health and Environmental Sciences, University of L'Aquila, 67100 L'Aquila, Italy
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Lee BH, Cho JH, Kwon BH, Lee M, Lee SW. Iteratively Calibratable Network for Reliable EEG-Based Robotic Arm Control. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2793-2804. [PMID: 39074028 DOI: 10.1109/tnsre.2024.3434983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/31/2024]
Abstract
Robotic arms are increasingly being utilized in shared workspaces, which necessitates the accurate interpretation of human intentions for both efficiency and safety. Electroencephalogram (EEG) signals, commonly employed to measure brain activity, offer a direct communication channel between humans and robotic arms. However, the ambiguous and unstable characteristics of EEG signals, coupled with their widespread distribution, make it challenging to collect sufficient data and hinder the calibration performance for new signals, thereby reducing the reliability of EEG-based applications. To address these issues, this study proposes an iteratively calibratable network aimed at enhancing the reliability and efficiency of EEG-based robotic arm control systems. The proposed method integrates feature inputs with network expansion techniques. This integration allows a network trained on an extensive initial dataset to adapt effectively to new users during calibration. Additionally, our approach combines motor imagery and speech imagery datasets to increase not only its intuitiveness but also the number of command classes. The evaluation is conducted in a pseudo-online manner, with a robotic arm operating in real-time to collect data, which is then analyzed offline. The evaluation results demonstrated that the proposed method outperformed the comparison group in 10 sessions and demonstrated competitive results when the two paradigms were combined. Therefore, it was confirmed that the network can be calibrated and personalized using only the new data from new users.
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Dillen A, Omidi M, Díaz MA, Ghaffari F, Roelands B, Vanderborght B, Romain O, De Pauw K. Evaluating the real-world usability of BCI control systems with augmented reality: a user study protocol. Front Hum Neurosci 2024; 18:1448584. [PMID: 39161850 PMCID: PMC11330773 DOI: 10.3389/fnhum.2024.1448584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Accepted: 07/18/2024] [Indexed: 08/21/2024] Open
Abstract
Brain-computer interfaces (BCI) enable users to control devices through their brain activity. Motor imagery (MI), the neural activity resulting from an individual imagining performing a movement, is a common control paradigm. This study introduces a user-centric evaluation protocol for assessing the performance and user experience of an MI-based BCI control system utilizing augmented reality. Augmented reality is employed to enhance user interaction by displaying environment-aware actions, and guiding users on the necessary imagined movements for specific device commands. One of the major gaps in existing research is the lack of comprehensive evaluation methodologies, particularly in real-world conditions. To address this gap, our protocol combines quantitative and qualitative assessments across three phases. In the initial phase, the BCI prototype's technical robustness is validated. Subsequently, the second phase involves a performance assessment of the control system. The third phase introduces a comparative analysis between the prototype and an alternative approach, incorporating detailed user experience evaluations through questionnaires and comparisons with non-BCI control methods. Participants engage in various tasks, such as object sorting, picking and placing, and playing a board game using the BCI control system. The evaluation procedure is designed for versatility, intending applicability beyond the specific use case presented. Its adaptability enables easy customization to meet the specific user requirements of the investigated BCI control application. This user-centric evaluation protocol offers a comprehensive framework for iterative improvements to the BCI prototype, ensuring technical validation, performance assessment, and user experience evaluation in a systematic and user-focused manner.
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Affiliation(s)
- Arnau Dillen
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Équipes Traitement de l'Information et Systèmes, UMR 8051, CY Cergy Paris Université, École Nationale Supérieure de l'Électronique et de ses Applications (ENSEA), Centre national de la recherche scientifique (CNRS), Cergy, France
- Brussels Human Robotic Research Center (BruBotics), Vrije Universiteit Brussel, Brussels, Belgium
| | - Mohsen Omidi
- Brussels Human Robotic Research Center (BruBotics), Vrije Universiteit Brussel, Brussels, Belgium
- imec, Brussels, Belgium
| | - María Alejandra Díaz
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Brussels Human Robotic Research Center (BruBotics), Vrije Universiteit Brussel, Brussels, Belgium
| | - Fakhreddine Ghaffari
- Équipes Traitement de l'Information et Systèmes, UMR 8051, CY Cergy Paris Université, École Nationale Supérieure de l'Électronique et de ses Applications (ENSEA), Centre national de la recherche scientifique (CNRS), Cergy, France
| | - Bart Roelands
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Brussels Human Robotic Research Center (BruBotics), Vrije Universiteit Brussel, Brussels, Belgium
| | - Bram Vanderborght
- Brussels Human Robotic Research Center (BruBotics), Vrije Universiteit Brussel, Brussels, Belgium
- imec, Brussels, Belgium
| | - Olivier Romain
- Équipes Traitement de l'Information et Systèmes, UMR 8051, CY Cergy Paris Université, École Nationale Supérieure de l'Électronique et de ses Applications (ENSEA), Centre national de la recherche scientifique (CNRS), Cergy, France
| | - Kevin De Pauw
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Brussels Human Robotic Research Center (BruBotics), Vrije Universiteit Brussel, Brussels, Belgium
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Kiessner AK, Schirrmeister RT, Boedecker J, Ball T. Reaching the ceiling? Empirical scaling behaviour for deep EEG pathology classification. Comput Biol Med 2024; 178:108681. [PMID: 38878396 DOI: 10.1016/j.compbiomed.2024.108681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 05/29/2024] [Accepted: 05/30/2024] [Indexed: 07/24/2024]
Abstract
Machine learning techniques, particularly deep convolutional neural networks (ConvNets), are increasingly being used to automate clinical EEG analysis, with the potential to reduce the clinical burden and improve patient care. However, further research is required before they can be used in clinical settings, particularly regarding the impact of the number of training samples and model parameters on their testing error. To address this, we present a comprehensive study of the empirical scaling behaviour of ConvNets for EEG pathology classification. We analysed the testing error with increasing the training samples and model size for four different ConvNet architectures. The focus of our experiments is width scaling, and we have increased the number of parameters to up to 1.8 million. Our evaluation was based on two publicly available datasets: the Temple University Hospital (TUH) Abnormal EEG Corpus and the TUH Abnormal Expansion Balanced EEG Corpus, which together contain 10,707 training samples. The results show that the testing error follows a saturating power-law with both model and dataset size. This pattern is consistent across different datasets and ConvNet architectures. Furthermore, empirically observed accuracies saturate at 85%-87%, which may be due to an imperfect inter-rater agreement on the clinical labels. The empirical scaling behaviour of the test performance with dataset and model size has significant implications for deep EEG pathology classification research and practice. Our findings highlight the potential of deep ConvNets for high-performance EEG pathology classification, and the identified scaling relationships provide valuable recommendations for the advancement of automated EEG diagnostics.
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Affiliation(s)
- Ann-Kathrin Kiessner
- Neuromedical AI Lab, Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Engelbergerstr. 21, 79106, Freiburg, Germany; Neurorobotics Lab, Computer Science Department - University of Freiburg, Faculty of Engineering, University of Freiburg, Georges-Koehler-Allee 80, 79110, Freiburg, Germany; BrainLinks-BrainTools, Institute for Machine-Brain Interfacing Technology, University of Freiburg, Georges-Koehler-Allee 201, 79110, Freiburg, Germany.
| | - Robin T Schirrmeister
- Neuromedical AI Lab, Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Engelbergerstr. 21, 79106, Freiburg, Germany; BrainLinks-BrainTools, Institute for Machine-Brain Interfacing Technology, University of Freiburg, Georges-Koehler-Allee 201, 79110, Freiburg, Germany; Machine Learning Lab, Computer Science Department - University of Freiburg, Faculty of Engineering, University of Freiburg, Georges-Koehler-Allee 74, 79110, Freiburg, Germany
| | - Joschka Boedecker
- Neurorobotics Lab, Computer Science Department - University of Freiburg, Faculty of Engineering, University of Freiburg, Georges-Koehler-Allee 80, 79110, Freiburg, Germany; BrainLinks-BrainTools, Institute for Machine-Brain Interfacing Technology, University of Freiburg, Georges-Koehler-Allee 201, 79110, Freiburg, Germany; Collaborative Research Institute Intelligent Oncology (CRIION), Freiburger Innovationszentrum (FRIZ) Building, Georges-Koehler-Allee 302, 79110, Freiburg, Germany
| | - Tonio Ball
- Neuromedical AI Lab, Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Engelbergerstr. 21, 79106, Freiburg, Germany; BrainLinks-BrainTools, Institute for Machine-Brain Interfacing Technology, University of Freiburg, Georges-Koehler-Allee 201, 79110, Freiburg, Germany; Freiburg Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher Str. 64, 79106, Freiburg, Germany
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Pfeffer MA, Ling SSH, Wong JKW. Exploring the frontier: Transformer-based models in EEG signal analysis for brain-computer interfaces. Comput Biol Med 2024; 178:108705. [PMID: 38865781 DOI: 10.1016/j.compbiomed.2024.108705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 05/01/2024] [Accepted: 06/01/2024] [Indexed: 06/14/2024]
Abstract
This review systematically explores the application of transformer-based models in EEG signal processing and brain-computer interface (BCI) development, with a distinct focus on ensuring methodological rigour and adhering to empirical validations within the existing literature. By examining various transformer architectures, such as the Temporal Spatial Transformer Network (TSTN) and EEG Conformer, this review delineates their capabilities in mitigating challenges intrinsic to EEG data, such as noise and artifacts, and their subsequent implications on decoding and classification accuracies across disparate mental tasks. The analytical scope extends to a meticulous examination of attention mechanisms within transformer models, delineating their role in illuminating critical temporal and spatial EEG features and facilitating interpretability in model decision-making processes. The discourse additionally encapsulates emerging works that substantiate the efficacy of transformer models in noise reduction of EEG signals and diversifying applications beyond the conventional motor imagery paradigm. Furthermore, this review elucidates evident gaps and propounds exploratory avenues in the applications of pre-trained transformers in EEG analysis and the potential expansion into real-time and multi-task BCI applications. Collectively, this review distils extant knowledge, navigates through the empirical findings, and puts forward a structured synthesis, thereby serving as a conduit for informed future research endeavours in transformer-enhanced, EEG-based BCI systems.
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Affiliation(s)
- Maximilian Achim Pfeffer
- Faculty of Engineering and Information Technology, University of Technology Sydney, CB11 81-113, Broadway, Ultimo, 2007, New South Wales, Australia.
| | - Steve Sai Ho Ling
- Faculty of Engineering and Information Technology, University of Technology Sydney, CB11 81-113, Broadway, Ultimo, 2007, New South Wales, Australia.
| | - Johnny Kwok Wai Wong
- Faculty of Design, Architecture and Building, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia.
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Bomatter P, Paillard J, Garces P, Hipp J, Engemann DA. Machine learning of brain-specific biomarkers from EEG. EBioMedicine 2024; 106:105259. [PMID: 39106531 DOI: 10.1016/j.ebiom.2024.105259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 07/05/2024] [Accepted: 07/11/2024] [Indexed: 08/09/2024] Open
Abstract
BACKGROUND Electroencephalography (EEG) has a long history as a clinical tool to study brain function, and its potential to derive biomarkers for various applications is far from exhausted. Machine learning (ML) can guide future innovation by harnessing the wealth of complex EEG signals to isolate relevant brain activity. Yet, ML studies in EEG tend to ignore physiological artefacts, which may cause problems for deriving biomarkers specific to the central nervous system (CNS). METHODS We present a framework for conceptualising machine learning from CNS versus peripheral signals measured with EEG. A signal representation based on Morlet wavelets allowed us to define traditional brain activity features (e.g. log power) and alternative inputs used by state-of-the-art ML approaches based on covariance matrices. Using more than 2600 EEG recordings from large public databases (TUAB, TDBRAIN), we studied the impact of peripheral signals and artefact removal techniques on ML models in age and sex prediction analyses. FINDINGS Across benchmarks, basic artefact rejection improved model performance, whereas further removal of peripheral signals using ICA decreased performance. Our analyses revealed that peripheral signals enable age and sex prediction. However, they explained only a fraction of the performance provided by brain signals. INTERPRETATION We show that brain signals and body signals, both present in the EEG, allow for prediction of personal characteristics. While these results may depend on specific applications, our work suggests that great care is needed to separate these signals when the goal is to develop CNS-specific biomarkers using ML. FUNDING All authors have been working for F. Hoffmann-La Roche Ltd.
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Affiliation(s)
- Philipp Bomatter
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Joseph Paillard
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Pilar Garces
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Jörg Hipp
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Denis-Alexander Engemann
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland.
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Duan T, Wang Z, Li F, Doretto G, Adjeroh DA, Yin Y, Tao C. Online continual decoding of streaming EEG signal with a balanced and informative memory buffer. Neural Netw 2024; 176:106338. [PMID: 38692190 DOI: 10.1016/j.neunet.2024.106338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 03/20/2024] [Accepted: 04/23/2024] [Indexed: 05/03/2024]
Abstract
Electroencephalography (EEG) based Brain Computer Interface (BCI) systems play a significant role in facilitating how individuals with neurological impairments effectively interact with their environment. In real world applications of BCI system for clinical assistance and rehabilitation training, the EEG classifier often needs to learn on sequentially arriving subjects in an online manner. As patterns of EEG signals can be significantly different for different subjects, the EEG classifier can easily erase knowledge of learnt subjects after learning on later ones as it performs decoding in online streaming scenario, namely catastrophic forgetting. In this work, we tackle this problem with a memory-based approach, which considers the following conditions: (1) subjects arrive sequentially in an online manner, with no large scale dataset available for joint training beforehand, (2) data volume from the different subjects could be imbalanced, (3) decoding difficulty of the sequential streaming signal vary, (4) continual classification for a long time is required. This online sequential EEG decoding problem is more challenging than classic cross subject EEG decoding as there is no large-scale training data from the different subjects available beforehand. The proposed model keeps a small balanced memory buffer during sequential learning, with memory data dynamically selected based on joint consideration of data volume and informativeness. Furthermore, for the more general scenarios where subject identity is unknown to the EEG decoder, aka. subject agnostic scenario, we propose a kernel based subject shift detection method that identifies underlying subject changes on the fly in a computationally efficient manner. We develop challenging benchmarks of streaming EEG data from sequentially arriving subjects with both balanced and imbalanced data volumes, and performed extensive experiments with a detailed ablation study on the proposed model. The results show the effectiveness of our proposed approach, enabling the decoder to maintain performance on all previously seen subjects over a long period of sequential decoding. The model demonstrates the potential for real-world applications.
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Affiliation(s)
- Tiehang Duan
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, 32246 United States
| | - Zhenyi Wang
- Department of Computer Science, University of Maryland, College Park, MD, 20742, United States
| | - Fang Li
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, 32246 United States
| | - Gianfranco Doretto
- Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, 26506, United States
| | - Donald A Adjeroh
- Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, 26506, United States.
| | - Yiyi Yin
- Meta AI, Seattle, WA, 98005, United States
| | - Cui Tao
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, 32246 United States.
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Mahjoory K, Bahmer A, Henry MJ. Convolutional neural networks can identify brain interactions involved in decoding spatial auditory attention. PLoS Comput Biol 2024; 20:e1012376. [PMID: 39116183 PMCID: PMC11335149 DOI: 10.1371/journal.pcbi.1012376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 08/20/2024] [Accepted: 07/30/2024] [Indexed: 08/10/2024] Open
Abstract
Human listeners have the ability to direct their attention to a single speaker in a multi-talker environment. The neural correlates of selective attention can be decoded from a single trial of electroencephalography (EEG) data. In this study, leveraging the source-reconstructed and anatomically-resolved EEG data as inputs, we sought to employ CNN as an interpretable model to uncover task-specific interactions between brain regions, rather than simply to utilize it as a black box decoder. To this end, our CNN model was specifically designed to learn pairwise interaction representations for 10 cortical regions from five-second inputs. By exclusively utilizing these features for decoding, our model was able to attain a median accuracy of 77.56% for within-participant and 65.14% for cross-participant classification. Through ablation analysis together with dissecting the features of the models and applying cluster analysis, we were able to discern the presence of alpha-band-dominated inter-hemisphere interactions, as well as alpha- and beta-band dominant interactions that were either hemisphere-specific or were characterized by a contrasting pattern between the right and left hemispheres. These interactions were more pronounced in parietal and central regions for within-participant decoding, but in parietal, central, and partly frontal regions for cross-participant decoding. These findings demonstrate that our CNN model can effectively utilize features known to be important in auditory attention tasks and suggest that the application of domain knowledge inspired CNNs on source-reconstructed EEG data can offer a novel computational framework for studying task-relevant brain interactions.
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Affiliation(s)
- Keyvan Mahjoory
- Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - Andreas Bahmer
- RheinMain University of Applied Sciences Campus Ruesselsheim, Wiesbaden, Germany
| | - Molly J. Henry
- Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
- Department of Psychology, Toronto Metropolitan University, Toronto, Ontario, Canada
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Kim M, Im CH. HiRENet: Novel convolutional neural network architecture using Hilbert-transformed and raw electroencephalogram (EEG) for subject-independent emotion classification. Comput Biol Med 2024; 178:108788. [PMID: 38941902 DOI: 10.1016/j.compbiomed.2024.108788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 06/30/2024]
Abstract
BACKGROUND AND OBJECTIVES Convolutional neural networks (CNNs) are the most widely used deep-learning framework for decoding electroencephalograms (EEGs) due to their exceptional ability to extract hierarchical features from high-dimensional EEG data. Traditionally, CNNs have primarily utilized multi-channel raw EEG data as the input tensor; however, the performance of CNN-based EEG decoding may be enhanced by incorporating phase information alongside amplitude information. METHODS This study introduces a novel CNN architecture called the Hilbert-transformed (HT) and raw EEG network (HiRENet), which incorporates both raw and HT EEG as inputs. This concurrent use of HT and raw EEG aims to integrate phase information with existing amplitude information, potentially offering a more comprehensive reflection of functional connectivity across various brain regions. The HiRENet model was developed using two CNN frameworks: ShallowFBCSPNet and a CNN with a residual block (ResCNN). The performance of the HiRENet model was assessed using a lab-made EEG database to classify human emotions, comparing three input modalities: raw EEG, HT EEG, and a combination of both signals. Additionally, the computational complexity was evaluated to validate the computational efficiency of the ResCNN design. RESULTS The HiRENet model based on ResCNN achieved the highest classification accuracy, with 86.03% for valence and 84.01% for arousal classifications, surpassing traditional CNN methodologies. Considering computational efficiency, ResCNN demonstrated superiority over ShallowFBCSPNet in terms of speed and inference time, despite having a higher parameter count. CONCLUSION Our experimental results showed that the proposed HiRENet can be potentially used as a new option to improve the overall performance for deep learning-based EEG decoding problems.
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Affiliation(s)
- Minsu Kim
- Department of Artificial Intelligence, Hanyang University, Seoul, Republic of Korea.
| | - Chang-Hwan Im
- Department of Artificial Intelligence, Hanyang University, Seoul, Republic of Korea; Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea; Department of Electronics Engineering, Hanyang University, Seoul, Republic of Korea.
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Chiou N, Günal M, Koyejo S, Perpetuini D, Chiarelli AM, Low KA, Fabiani M, Gratton G. Single-Trial Detection and Classification of Event-Related Optical Signals for a Brain-Computer Interface Application. Bioengineering (Basel) 2024; 11:781. [PMID: 39199739 PMCID: PMC11351476 DOI: 10.3390/bioengineering11080781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2024] [Revised: 07/23/2024] [Accepted: 07/27/2024] [Indexed: 09/01/2024] Open
Abstract
Event-related optical signals (EROS) measure fast modulations in the brain's optical properties related to neuronal activity. EROS offer a high spatial and temporal resolution and can be used for brain-computer interface (BCI) applications. However, the ability to classify single-trial EROS remains unexplored. This study evaluates the performance of neural network methods for single-trial classification of motor response-related EROS. EROS activity was obtained from a high-density recording montage covering the motor cortex during a two-choice reaction time task involving responses with the left or right hand. This study utilized a convolutional neural network (CNN) approach to extract spatiotemporal features from EROS data and perform classification of left and right motor responses. Subject-specific classifiers trained on EROS phase data outperformed those trained on intensity data, reaching an average single-trial classification accuracy of around 63%. Removing low-frequency noise from intensity data is critical for achieving discriminative classification results with this measure. Our results indicate that deep learning with high-spatial-resolution signals, such as EROS, can be successfully applied to single-trial classifications.
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Affiliation(s)
- Nicole Chiou
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA;
| | - Mehmet Günal
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana, Champaign, Urbana, IL 61801, USA; (M.G.); (K.A.L.); (M.F.); (G.G.)
| | - Sanmi Koyejo
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA;
| | - David Perpetuini
- Department of Engineering and Geology, “G. D’Annunzio University” of Chieti-Pescara, 65127 Pescara, Italy;
| | - Antonio Maria Chiarelli
- Department of Neuroscience, Imaging and Clinical Sciences, “G. D’Annunzio University” of Chieti-Pescara, 66100 Chieti, Italy;
- Institute for Advanced Biomedical Technologies, “G. D’Annunzio University” of Chieti-Pescara, 66100 Chieti, Italy
| | - Kathy A. Low
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana, Champaign, Urbana, IL 61801, USA; (M.G.); (K.A.L.); (M.F.); (G.G.)
| | - Monica Fabiani
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana, Champaign, Urbana, IL 61801, USA; (M.G.); (K.A.L.); (M.F.); (G.G.)
- Psychology Department, University of Illinois Urbana, Champaign, Champaign, IL 61820, USA
| | - Gabriele Gratton
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana, Champaign, Urbana, IL 61801, USA; (M.G.); (K.A.L.); (M.F.); (G.G.)
- Psychology Department, University of Illinois Urbana, Champaign, Champaign, IL 61820, USA
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Wang H, Chen P, Zhang M, Zhang J, Sun X, Li M, Yang X, Gao Z. EEG-Based Motor Imagery Recognition Framework via Multisubject Dynamic Transfer and Iterative Self-Training. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:10698-10712. [PMID: 37027653 DOI: 10.1109/tnnls.2023.3243339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
A robust decoding model that can efficiently deal with the subject and period variation is urgently needed to apply the brain-computer interface (BCI) system. The performance of most electroencephalogram (EEG) decoding models depends on the characteristics of specific subjects and periods, which require calibration and training with annotated data prior to application. However, this situation will become unacceptable as it would be difficult for subjects to collect data for an extended period, especially in the rehabilitation process of disability based on motor imagery (MI). To address this issue, we propose an unsupervised domain adaptation framework called iterative self-training multisubject domain adaptation (ISMDA) that focuses on the offline MI task. First, the feature extractor is purposefully designed to map the EEG to a latent space of discriminative representations. Second, the attention module based on dynamic transfer matches the source domain and target domain samples with a higher coincidence degree in latent space. Then, an independent classifier oriented to the target domain is employed in the first stage of the iterative training process to cluster the samples of the target domain through similarity. Finally, a pseudolabel algorithm based on certainty and confidence is employed in the second stage of the iterative training process to adequately calibrate the error between prediction and empirical probabilities. To evaluate the effectiveness of the model, extensive testing has been performed on three publicly available MI datasets, the BCI IV IIa, the High gamma dataset, and Kwon et al. datasets. The proposed method achieved 69.51%, 82.38%, and 90.98% cross-subject classification accuracy on the three datasets, which outperforms the current state-of-the-art offline algorithms. Meanwhile, all results demonstrated that the proposed method could address the main challenges of the offline MI paradigm.
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Guenther S, Kosmyna N, Maes P. Image classification and reconstruction from low-density EEG. Sci Rep 2024; 14:16436. [PMID: 39013929 PMCID: PMC11252274 DOI: 10.1038/s41598-024-66228-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 06/28/2024] [Indexed: 07/18/2024] Open
Abstract
Recent advances in visual decoding have enabled the classification and reconstruction of perceived images from the brain. However, previous approaches have predominantly relied on stationary, costly equipment like fMRI or high-density EEG, limiting the real-world availability and applicability of such projects. Additionally, several EEG-based paradigms have utilized artifactual, rather than stimulus-related information yielding flawed classification and reconstruction results. Our goal was to reduce the cost of the decoding paradigm, while increasing its flexibility. Therefore, we investigated whether the classification of an image category and the reconstruction of the image itself is possible from the visually evoked brain activity measured by a portable, 8-channel EEG. To compensate for the low electrode count and to avoid flawed predictions, we designed a theory-guided EEG setup and created a new experiment to obtain a dataset from 9 subjects. We compared five contemporary classification models with our setup reaching an average accuracy of 34.4% for 20 image classes on hold-out test recordings. For the reconstruction, the top-performing model was used as an EEG-encoder which was combined with a pretrained latent diffusion model via double-conditioning. After fine-tuning, we reconstructed images from the test set with a 1000 trial 50-class top-1 accuracy of 35.3%. While not reaching the same performance as MRI-based paradigms on unseen stimuli, our approach greatly improved the affordability and mobility of the visual decoding technology.
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Affiliation(s)
- Sven Guenther
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
| | - Nataliya Kosmyna
- Media Lab, Massachusetts Institute of Technology, Cambridge, USA
| | - Pattie Maes
- Media Lab, Massachusetts Institute of Technology, Cambridge, USA
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Wang Z, Yu J, Gao J, Bai Y, Wan Z. MutaPT: A Multi-Task Pre-Trained Transformer for Classifying State of Disorders of Consciousness Using EEG Signal. Brain Sci 2024; 14:688. [PMID: 39061428 PMCID: PMC11274898 DOI: 10.3390/brainsci14070688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 05/22/2024] [Accepted: 05/25/2024] [Indexed: 07/28/2024] Open
Abstract
Deep learning (DL) has been demonstrated to be a valuable tool for classifying state of disorders of consciousness (DOC) using EEG signals. However, the performance of the DL-based DOC state classification is often challenged by the limited size of EEG datasets. To overcome this issue, we introduce multiple open-source EEG datasets to increase data volume and train a novel multi-task pre-training Transformer model named MutaPT. Furthermore, we propose a cross-distribution self-supervised (CDS) pre-training strategy to enhance the model's generalization ability, addressing data distribution shifts across multiple datasets. An EEG dataset of DOC patients is used to validate the effectiveness of our methods for the task of classifying DOC states. Experimental results show the superiority of our MutaPT over several DL models for EEG classification.
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Affiliation(s)
- Zihan Wang
- School of Information Engineering, Nanchang University, Nanchang 330031, China
| | - Junqi Yu
- School of Information Engineering, Nanchang University, Nanchang 330031, China
| | - Jiahui Gao
- School of Public Policy and Administration, Nanchang University, Nanchang 330031, China
| | - Yang Bai
- Affiliated Rehabilitation Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330031, China
| | - Zhijiang Wan
- School of Information Engineering, Nanchang University, Nanchang 330031, China
- Industrial Institute of Artificial Intelligence, Nanchang University, Nanchang 330031, China
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50
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Ahuja C, Sethia D. Harnessing Few-Shot Learning for EEG signal classification: a survey of state-of-the-art techniques and future directions. Front Hum Neurosci 2024; 18:1421922. [PMID: 39050382 PMCID: PMC11266297 DOI: 10.3389/fnhum.2024.1421922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 05/31/2024] [Indexed: 07/27/2024] Open
Abstract
This paper presents a systematic literature review, providing a comprehensive taxonomy of Data Augmentation (DA), Transfer Learning (TL), and Self-Supervised Learning (SSL) techniques within the context of Few-Shot Learning (FSL) for EEG signal classification. EEG signals have shown significant potential in various paradigms, including Motor Imagery, Emotion Recognition, Visual Evoked Potentials, Steady-State Visually Evoked Potentials, Rapid Serial Visual Presentation, Event-Related Potentials, and Mental Workload. However, challenges such as limited labeled data, noise, and inter/intra-subject variability have impeded the effectiveness of traditional machine learning (ML) and deep learning (DL) models. This review methodically explores how FSL approaches, incorporating DA, TL, and SSL, can address these challenges and enhance classification performance in specific EEG paradigms. It also delves into the open research challenges related to these techniques in EEG signal classification. Specifically, the review examines the identification of DA strategies tailored to various EEG paradigms, the creation of TL architectures for efficient knowledge transfer, and the formulation of SSL methods for unsupervised representation learning from EEG data. Addressing these challenges is crucial for enhancing the efficacy and robustness of FSL-based EEG signal classification. By presenting a structured taxonomy of FSL techniques and discussing the associated research challenges, this systematic review offers valuable insights for future investigations in EEG signal classification. The findings aim to guide and inspire researchers, promoting advancements in applying FSL methodologies for improved EEG signal analysis and classification in real-world settings.
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Affiliation(s)
- Chirag Ahuja
- Department of Computer Science and Engineering, Delhi Technological University, New Delhi, India
| | - Divyashikha Sethia
- Department of Software Engineering, Delhi Technology University, New Delhi, India
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