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Deng H, Li M, Li J, Guo M, Xu G. A robust multi-branch multi-attention-mechanism EEGNet for motor imagery BCI decoding. J Neurosci Methods 2024; 405:110108. [PMID: 38458260 DOI: 10.1016/j.jneumeth.2024.110108] [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: 11/05/2023] [Revised: 02/28/2024] [Accepted: 03/05/2024] [Indexed: 03/10/2024]
Abstract
BACKGROUND Motor-Imagery-based Brain-Computer Interface (MI-BCI) is a promising technology to assist communication, movement, and neurological rehabilitation for motor-impaired individuals. Electroencephalography (EEG) decoding techniques using deep learning (DL) possess noteworthy advantages due to automatic feature extraction and end-to-end learning. However, the DL-based EEG decoding models tend to show large variations due to intersubject variability of EEG, which results from inconsistencies of different subjects' optimal hyperparameters. NEW METHODS This study proposes a multi-branch multi-attention mechanism EEGNet model (MBMANet) for robust decoding. It applies the multi-branch EEGNet structure to achieve various feature extractions. Further, the different attention mechanisms introduced in each branch attain diverse adaptive weight adjustments. This combination of multi-branch and multi-attention mechanisms allows for multi-level feature fusion to provide robust decoding for different subjects. RESULTS The MBMANet model has a four-classification accuracy of 83.18% and kappa of 0.776 on the BCI Competition IV-2a dataset, which outperforms other eight CNN-based decoding models. This consistently satisfactory performance across all nine subjects indicates that the proposed model is robust. CONCLUSIONS The combine of multi-branch and multi-attention mechanisms empowers the DL-based models to adaptively learn different EEG features, which provides a feasible solution for dealing with data variability. It also gives the MBMANet model more accurate decoding of motion intentions and lower training costs, thus improving the MI-BCI's utility and robustness.
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Affiliation(s)
- Haodong Deng
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China; Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin 300132, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300132, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China
| | - Mengfan Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China; Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin 300132, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300132, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China.
| | - Jundi Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China; Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin 300132, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300132, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China
| | - Miaomiao Guo
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China; Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin 300132, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300132, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China; Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin 300132, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300132, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China
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Wang H, Jiang J, Gan JQ, Wang H. Motor Imagery EEG Classification Based on a Weighted Multi-Branch Structure Suitable for Multisubject Data. IEEE Trans Biomed Eng 2023; 70:3040-3051. [PMID: 37186527 DOI: 10.1109/tbme.2023.3274231] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
OBJECTIVE Electroencephalogram (EEG) signal recognition based on deep learning technology requires the support of sufficient data. However, training data scarcity usually occurs in subject-specific motor imagery tasks unless multisubject data can be used to enlarge training data. Unfortunately, because of the large discrepancies between data distributions from different subjects, model performance could only be improved marginally or even worsened by simply training on multisubject data. METHOD This article proposes a novel weighted multi-branch (WMB) structure for handling multisubject data to solve the problem, in which each branch is responsible for fitting a pair of source-target subject data and adaptive weights are used to integrate all branches or select branches with the largest weights to make the final decision. The proposed WMB structure was applied to six well-known deep learning models (EEGNet, Shallow ConvNet, Deep ConvNet, ResNet, MSFBCNN, and EEG_TCNet) and comprehensive experiments were conducted on EEG datasets BCICIV-2a, BCICIV-2b, high gamma dataset (HGD) and two supplementary datasets. RESULT Superior results against the state-of-the-art models have demonstrated the efficacy of the proposed method in subject-specific motor imagery EEG classification. For example, the proposed WMB_EEGNet achieved classification accuracies of 84.14%, 90.23%, and 97.81% on BCICIV-2a, BCICIV-2b and HGD, respectively. CONCLUSION It is clear that the proposed WMB structure is capable to make good use of multisubject data with large distribution discrepancies for subject-specific EEG classification.
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Yang L, Shi T, Lv J, Liu Y, Dai Y, Zou L. A multi-feature fusion decoding study for unilateral upper-limb fine motor imagery. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:2482-2500. [PMID: 36899543 DOI: 10.3934/mbe.2023116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
To address the fact that the classical motor imagination paradigm has no noticeable effect on the rehabilitation training of upper limbs in patients after stroke and the corresponding feature extraction algorithm is limited to a single domain, this paper describes the design of a unilateral upper-limb fine motor imagination paradigm and the collection of data from 20 healthy people. It presents a feature extraction algorithm for multi-domain fusion and compares the common spatial pattern (CSP), improved multiscale permutation entropy (IMPE) and multi-domain fusion features of all participants through the use of decision tree, linear discriminant analysis, naive Bayes, a support vector machine, k-nearest neighbor and ensemble classification precision algorithms in the ensemble classifier. For the same subject, the average classification accuracy improvement of the same classifier for multi-domain feature extraction relative to CSP feature results went up by 1.52%. The average classification accuracy improvement of the same classifier went up by 32.87% relative to the IMPE feature classification results. This study's unilateral fine motor imagery paradigm and multi-domain feature fusion algorithm provide new ideas for upper limb rehabilitation after stroke.
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Affiliation(s)
- Liangyu Yang
- The School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Tianyu Shi
- The School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Jidong Lv
- The School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Yan Liu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Department of Medical Image, Suzhou 215163, China
- Suzhou Guokekangcheng Medical Technique Co., Ltd., Suzhou 215163, China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Department of Medical Image, Suzhou 215163, China
- Suzhou Guokekangcheng Medical Technique Co., Ltd., Suzhou 215163, China
| | - Ling Zou
- The School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China
- Key Laboratory of Brain Machine Collaborative Intelligence Foundation of Zhejiang Province, Hangzhou, Zhejiang 310018, China
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A novel feature extraction method using chemosensory EEG for Parkinson's disease classification. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Jiang L, He J, Pan H, Wu D, Jiang T, Liu J. Seizure detection algorithm based on improved functional brain network structure feature extraction. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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Wang C, Wu Y, Wang C, Zhu Y, Wang C, Niu Y, Shao Z, Gao X, Zhao Z, Yu Y. MI-EEG classification using Shannon complex wavelet and convolutional neural networks. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Xu DQ, Li MA. A dual alignment-based multi-source domain adaptation framework for motor imagery EEG classification. APPL INTELL 2022; 53:10766-10788. [PMID: 36039116 PMCID: PMC9402410 DOI: 10.1007/s10489-022-04077-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/08/2022] [Indexed: 11/25/2022]
Affiliation(s)
- Dong-qin Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 China
| | - Ming-ai Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124 China
- Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124 China
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Qiu L, Zhong Y, He Z, Pan J. Improved classification performance of EEG-fNIRS multimodal brain-computer interface based on multi-domain features and multi-level progressive learning. Front Hum Neurosci 2022; 16:973959. [PMID: 35992956 PMCID: PMC9388144 DOI: 10.3389/fnhum.2022.973959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 07/15/2022] [Indexed: 11/13/2022] Open
Abstract
Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have potentially complementary characteristics that reflect the electrical and hemodynamic characteristics of neural responses, so EEG-fNIRS-based hybrid brain-computer interface (BCI) is the research hotspots in recent years. However, current studies lack a comprehensive systematic approach to properly fuse EEG and fNIRS data and exploit their complementary potential, which is critical for improving BCI performance. To address this issue, this study proposes a novel multimodal fusion framework based on multi-level progressive learning with multi-domain features. The framework consists of a multi-domain feature extraction process for EEG and fNIRS, a feature selection process based on atomic search optimization, and a multi-domain feature fusion process based on multi-level progressive machine learning. The proposed method was validated on EEG-fNIRS-based motor imagery (MI) and mental arithmetic (MA) tasks involving 29 subjects, and the experimental results show that multi-domain features provide better classification performance than single-domain features, and multi-modality provides better classification performance than single-modality. Furthermore, the experimental results and comparison with other methods demonstrated the effectiveness and superiority of the proposed method in EEG and fNIRS information fusion, it can achieve an average classification accuracy of 96.74% in the MI task and 98.42% in the MA task. Our proposed method may provide a general framework for future fusion processing of multimodal brain signals based on EEG-fNIRS.
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Zheng L, Feng W, Ma Y, Lian P, Xiao Y, Yi Z, Wu X. Ensemble learning method based on temporal, spatial features with multi-scale filter banks for motor imagery EEG classification. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103634] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Zhang R, Zeng Y, Tong L, Shu J, Lu R, Li Z, Yang K, Yan B. EEG Identity Authentication in Multi-Domain Features: A Multi-Scale 3D-CNN Approach. Front Neurorobot 2022; 16:901765. [PMID: 35783367 PMCID: PMC9243312 DOI: 10.3389/fnbot.2022.901765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 05/19/2022] [Indexed: 11/13/2022] Open
Abstract
Electroencephalogram (EEG) authentication has become a research hotspot in the field of information security due to its advantages of living, internal, and anti-stress. However, the performance of identity authentication system is limited by the inherent attributes of EEG, such as low SNR, low stability, and strong randomness. Researchers generally believe that the in-depth fusion of features can improve the performance of identity authentication and have explored among various feature domains. This experiment invited 70 subjects to participate in the EEG identity authentication task, and the experimental materials were visual stimuli of the self and non-self-names. This paper proposes an innovative EEG authentication framework, including efficient three-dimensional representation of EEG signals, multi-scale convolution structure, and the combination of multiple authentication strategies. In this work, individual EEG signals are converted into spatial–temporal–frequency domain three-dimensional forms to provide multi-angle mixed feature representation. Then, the individual identity features are extracted by the various convolution kernel of multi-scale vision, and the strategy of combining multiple convolution kernels is explored. The results show that the small-size and long-shape convolution kernel is suitable for ERP tasks, which can obtain better convergence and accuracy. The experimental results show that the classification performance of the proposed framework is excellent, and the multi-scale convolution method is effective to extract high-quality identity characteristics across feature domains. The results show that the branch number matches the EEG component number can obtain the excellent cost performance. In addition, this paper explores the network training performance for multi-scale module combination strategy and provides reference for deep network construction strategy of EEG signal processing.
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Affiliation(s)
- Rongkai Zhang
- Henan Key Laboratory of Imaging and Intelligent Processing, People's Liberation Army (PLA) Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Ying Zeng
- Henan Key Laboratory of Imaging and Intelligent Processing, People's Liberation Army (PLA) Strategic Support Force Information Engineering University, Zhengzhou, China
- Key Laboratory for Neuro Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Li Tong
- Henan Key Laboratory of Imaging and Intelligent Processing, People's Liberation Army (PLA) Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Jun Shu
- Henan Key Laboratory of Imaging and Intelligent Processing, People's Liberation Army (PLA) Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Runnan Lu
- Henan Key Laboratory of Imaging and Intelligent Processing, People's Liberation Army (PLA) Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Zhongrui Li
- Henan Key Laboratory of Imaging and Intelligent Processing, People's Liberation Army (PLA) Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Kai Yang
- Henan Key Laboratory of Imaging and Intelligent Processing, People's Liberation Army (PLA) Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Bin Yan
- Henan Key Laboratory of Imaging and Intelligent Processing, People's Liberation Army (PLA) Strategic Support Force Information Engineering University, Zhengzhou, China
- *Correspondence: Bin Yan
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Discriminative Frequencies and Temporal EEG Segmentation in the Motor Imagery Classification Approach. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12052736] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
A linear discriminant analysis transformation-based approach to the classification of three different motor imagery types for brain–computer interfaces was considered. The study involved 16 conditionally healthy subjects (12 men, 4 women, mean age of 21.5 years). First, the search for subject-specific discriminative frequencies was conducted in the task of movement-related activity. This procedure was shown to increase the classification accuracy compared to the conditional common spatial pattern (CSP) algorithm, followed by a linear classifier considered as a baseline approach. In addition, an original approach to finding discriminative temporal segments for each motor imagery was tested. This led to a further increase in accuracy under the conditions of using Hjorth parameters and interchannel correlation coefficients as features calculated for the EEG segments. In particular, classification by the latter feature led to the best accuracy of 71.6%, averaged over all subjects (intrasubject classification), and, surprisingly, it also allowed us to obtain a comparable value of intersubject classification accuracy of 68%. Furthermore, scatter plots demonstrated that two out of three pairs of motor imagery were discriminated by the approach presented.
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Huang B, Chen W, Lin CL, Juang CF, Wang J. MLP-BP: A novel framework for cuffless blood pressure measurement with PPG and ECG signals based on MLP-Mixer neural networks. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103404] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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