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Wang Z, Li S, Luo J, Liu J, Wu D. Channel reflection: Knowledge-driven data augmentation for EEG-based brain-computer interfaces. Neural Netw 2024; 176:106351. [PMID: 38713969 DOI: 10.1016/j.neunet.2024.106351] [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: 12/30/2023] [Revised: 04/04/2024] [Accepted: 04/28/2024] [Indexed: 05/09/2024]
Abstract
A brain-computer interface (BCI) enables direct communication between the human brain and external devices. Electroencephalography (EEG) based BCIs are currently the most popular for able-bodied users. To increase user-friendliness, usually a small amount of user-specific EEG data are used for calibration, which may not be enough to develop a pure data-driven decoding model. To cope with this typical calibration data shortage challenge in EEG-based BCIs, this paper proposes a parameter-free channel reflection (CR) data augmentation approach that incorporates prior knowledge on the channel distributions of different BCI paradigms in data augmentation. Experiments on eight public EEG datasets across four different BCI paradigms (motor imagery, steady-state visual evoked potential, P300, and seizure classifications) using different decoding algorithms demonstrated that: (1) CR is effective, i.e., it can noticeably improve the classification accuracy; (2) CR is robust, i.e., it consistently outperforms existing data augmentation approaches in the literature; and, (3) CR is flexible, i.e., it can be combined with other data augmentation approaches to further improve the performance. We suggest that data augmentation approaches like CR should be an essential step in EEG-based BCIs. Our code is available online.
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Affiliation(s)
- Ziwei Wang
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518063, China.
| | - Siyang Li
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518063, China.
| | - Jingwei Luo
- China Electronic System Technology Co., Ltd., Beijing 100089, China.
| | - Jiajing Liu
- School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Dongrui Wu
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518063, China.
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2
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Liu B, Gao H, Jiang Y, Wu J. Research on a soft saturation nonlinear SSVEP signal feature extraction algorithm. Sci Rep 2024; 14:17043. [PMID: 39048655 PMCID: PMC11269718 DOI: 10.1038/s41598-024-67853-6] [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: 04/26/2024] [Accepted: 07/16/2024] [Indexed: 07/27/2024] Open
Abstract
Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) have received widespread attention due to their high information transmission rate, high accuracy, and rich instruction set. However, the performance of its identification methods strongly depends on the amount of calibration data for within-subject classification. Some studies use deep learning (DL) algorithms for inter-subject classification, which can reduce the calculation process, but there is still much room for improvement in performance compared with intra-subject classification. To solve these problems, an efficient SSVEP signal recognition deep learning network model e-SSVEPNet based on the soft saturation nonlinear module is proposed in this paper. The soft saturation nonlinear module uses a similar exponential calculation method for output when it is less than zero, improving robustness to noise. Under the conditions of the SSVEP data set, two sliding time window lengths (1 s and 0.5 s), and three training data sizes, this paper evaluates the proposed network model and compares it with other traditional and deep learning model baseline methods. The experimental results of the nonlinear module were classified and compared. A large number of experimental results show that the proposed network has the highest average accuracy of intra-subject classification on the SSVEP data set, improves the performance of SSVEP signal classification and recognition, and has higher decoding accuracy under short signals, so it has huge potential ability to realize high-speed SSVEP-based for BCI.
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Affiliation(s)
- Bo Liu
- Shenyang Ligong University, Shenyang, Liaoning, China
| | - Hongwei Gao
- Shenyang Ligong University, Shenyang, Liaoning, China.
| | - Yueqiu Jiang
- Shenyang Ligong University, Shenyang, Liaoning, China.
| | - Jiaxuan Wu
- Shenyang Ligong University, Shenyang, Liaoning, China
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3
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Janardhan Reddy T, Ramasubba Reddy M. Narrow band-pass filtered canonical correlation analysis for frequency identification in SSVEP signals. Biomed Phys Eng Express 2024; 10:045047. [PMID: 38861953 DOI: 10.1088/2057-1976/ad567f] [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: 03/27/2024] [Accepted: 06/11/2024] [Indexed: 06/13/2024]
Abstract
Steady-state visual evoked potentials (SSVEP) are generated in the parieto-occipital regions, accompanied by background noise and artifacts. A strong pre-processing method is required to reduce this background noise and artifacts. This study proposed a narrow band-pass filtered canonical correlation analysis (NBPFCCA) to recognize frequency components in SSVEP signals. The proposed method is tested on the publicly available 40 stimulus frequencies dataset recorded from 35 subjects and 4 class in-house dataset acquired from 10 subjects. The performance of the proposed NBPFCCA method is compared with the standard canonical correlation analysis (CCA) and the filter bank CCA (FBCCA). The mean frequency detection accuracy of the standard CCA is 86.21% for the benchmark dataset, and it is improved to 95.58% in the proposed method. Results indicate that the proposed method significantly outperforms the standard canonical correlation analysis with an increase of 9.37% and 17% in frequency recognition accuracy of the benchmark and in-house datasets, respectively.
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Affiliation(s)
- T Janardhan Reddy
- Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai, 600036, India
| | - M Ramasubba Reddy
- Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai, 600036, India
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Li X, Yang S, Fei N, Wang J, Huang W, Hu Y. A Convolutional Neural Network for SSVEP Identification by Using a Few-Channel EEG. Bioengineering (Basel) 2024; 11:613. [PMID: 38927850 PMCID: PMC11200714 DOI: 10.3390/bioengineering11060613] [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: 04/03/2024] [Revised: 05/11/2024] [Accepted: 06/08/2024] [Indexed: 06/28/2024] Open
Abstract
The application of wearable electroencephalogram (EEG) devices is growing in brain-computer interfaces (BCI) owing to their good wearability and portability. Compared with conventional devices, wearable devices typically support fewer EEG channels. Devices with few-channel EEGs have been proven to be available for steady-state visual evoked potential (SSVEP)-based BCI. However, fewer-channel EEGs can cause the BCI performance to decrease. To address this issue, an attention-based complex spectrum-convolutional neural network (atten-CCNN) is proposed in this study, which combines a CNN with a squeeze-and-excitation block and uses the spectrum of the EEG signal as the input. The proposed model was assessed on a wearable 40-class dataset and a public 12-class dataset under subject-independent and subject-dependent conditions. The results show that whether using a three-channel EEG or single-channel EEG for SSVEP identification, atten-CCNN outperformed the baseline models, indicating that the new model can effectively enhance the performance of SSVEP-BCI with few-channel EEGs. Therefore, this SSVEP identification algorithm based on a few-channel EEG is particularly suitable for use with wearable EEG devices.
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Affiliation(s)
- Xiaodong Li
- Orthopedics Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China
| | - Shuoheng Yang
- Orthopedics Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China
| | - Ningbo Fei
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China
| | - Junlin Wang
- Orthopedics Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China
| | - Wei Huang
- Department of Rehabilitation, The Second Affiliated Hospital of Guangdong Medical University, Zhanjiang 524003, China
| | - Yong Hu
- Orthopedics Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China
- Department of Rehabilitation, The Second Affiliated Hospital of Guangdong Medical University, Zhanjiang 524003, China
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Chen SY, Chang CM, Chiang KJ, Wei CS. SSVEP-DAN: Cross-Domain Data Alignment for SSVEP-Based Brain-Computer Interfaces. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2027-2037. [PMID: 38781061 DOI: 10.1109/tnsre.2024.3404432] [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
Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency is highly dependent on individual training data acquired during time-consuming calibration sessions. To address the challenge of data insufficiency in SSVEP-based BCIs, we introduce SSVEP-DAN, the first dedicated neural network model designed to align SSVEP data across different domains, encompassing various sessions, subjects, or devices. Our experimental results demonstrate the ability of SSVEP-DAN to transform existing source SSVEP data into supplementary calibration data. This results in a significant improvement in SSVEP decoding accuracy while reducing the calibration time. We envision SSVEP-DAN playing a crucial role in future applications of high-performance SSVEP-based BCIs. The source code for this work is available at: https://github.com/CECNL/SSVEP-DAN.
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Zhang S, An D, Liu J, Chen J, Wei Y, Sun F. Dynamic decomposition graph convolutional neural network for SSVEP-based brain-computer interface. Neural Netw 2024; 172:106075. [PMID: 38278092 DOI: 10.1016/j.neunet.2023.12.029] [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: 09/18/2023] [Revised: 12/11/2023] [Accepted: 12/18/2023] [Indexed: 01/28/2024]
Abstract
The SSVEP-based paradigm serves as a prevalent approach in the realm of brain-computer interface (BCI). However, the processing of multi-channel electroencephalogram (EEG) data introduces challenges due to its non-Euclidean characteristic, necessitating methodologies that account for inter-channel topological relations. In this paper, we introduce the Dynamic Decomposition Graph Convolutional Neural Network (DDGCNN) designed for the classification of SSVEP EEG signals. Our approach incorporates layerwise dynamic graphs to address the oversmoothing issue in Graph Convolutional Networks (GCNs), employing a dense connection mechanism to mitigate the gradient vanishing problem. Furthermore, we enhance the traditional linear transformation inherent in GCNs with graph dynamic fusion, thereby elevating feature extraction and adaptive aggregation capabilities. Our experimental results demonstrate the effectiveness of proposed approach in learning and extracting features from EEG topological structure. The results shown that DDGCNN outperforms other state-of-the-art (SOTA) algorithms reported on two datasets (Dataset 1: 54 subjects, 4 targets, 2 sessions; Dataset 2: 35 subjects, 40 targets). Additionally, we showcase the implementation of DDGCNN in the context of synchronized BCI robotic fish control. This work represents a significant advancement in the field of EEG signal processing for SSVEP-based BCIs. Our proposed method processes SSVEP time domain signals directly as an end-to-end system, making it easy to deploy. The code is available at https://github.com/zshubin/DDGCNN.
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Affiliation(s)
- Shubin Zhang
- National Innovation Center for Digital Fishery, Beijing, 100083, China; Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Beijing, 100083, China; Ministry of Agriculture and Rural Affairs, Beijing, 100083, China; Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, Beijing, 100083, China; College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China.
| | - Dong An
- National Innovation Center for Digital Fishery, Beijing, 100083, China; Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Beijing, 100083, China; Ministry of Agriculture and Rural Affairs, Beijing, 100083, China; Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, Beijing, 100083, China; College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China.
| | - Jincun Liu
- National Innovation Center for Digital Fishery, Beijing, 100083, China; Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Beijing, 100083, China; Ministry of Agriculture and Rural Affairs, Beijing, 100083, China; Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, Beijing, 100083, China; College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China.
| | - Jiannan Chen
- Department of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei province, 066000, China.
| | - Yaoguang Wei
- National Innovation Center for Digital Fishery, Beijing, 100083, China; Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Beijing, 100083, China; Ministry of Agriculture and Rural Affairs, Beijing, 100083, China; Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, Beijing, 100083, China; College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China.
| | - Fuchun Sun
- Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.
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Liang L, Zhang Q, Zhou J, Li W, Gao X. Dataset Evaluation Method and Application for Performance Testing of SSVEP-BCI Decoding Algorithm. SENSORS (BASEL, SWITZERLAND) 2023; 23:6310. [PMID: 37514603 PMCID: PMC10385518 DOI: 10.3390/s23146310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 06/24/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023]
Abstract
Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems have been extensively researched over the past two decades, and multiple sets of standard datasets have been published and widely used. However, there are differences in sample distribution and collection equipment across different datasets, and there is a lack of a unified evaluation method. Most new SSVEP decoding algorithms are tested based on self-collected data or offline performance verification using one or two previous datasets, which can lead to performance differences when used in actual application scenarios. To address these issues, this paper proposed a SSVEP dataset evaluation method and analyzed six datasets with frequency and phase modulation paradigms to form an SSVEP algorithm evaluation dataset system. Finally, based on the above datasets, performance tests were carried out on the four existing SSVEP decoding algorithms. The findings reveal that the performance of the same algorithm varies significantly when tested on diverse datasets. Substantial performance variations were observed among subjects, ranging from the best-performing to the worst-performing. The above results demonstrate that the SSVEP dataset evaluation method can integrate six datasets to form a SSVEP algorithm performance testing dataset system. This system can test and verify the SSVEP decoding algorithm from different perspectives such as different subjects, different environments, and different equipment, which is helpful for the research of new SSVEP decoding algorithms and has significant reference value for other BCI application fields.
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Affiliation(s)
- Liyan Liang
- China Academy of Information and Communications Technology, Beijing 100161, China
| | - Qian Zhang
- China Academy of Information and Communications Technology, Beijing 100161, China
| | - Jie Zhou
- China Academy of Information and Communications Technology, Beijing 100161, China
| | - Wenyu Li
- China Academy of Information and Communications Technology, Beijing 100161, China
| | - Xiaorong Gao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
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Xu D, Tang F, Li Y, Zhang Q, Feng X. FB-CCNN: A Filter Bank Complex Spectrum Convolutional Neural Network with Artificial Gradient Descent Optimization. Brain Sci 2023; 13:brainsci13050780. [PMID: 37239253 DOI: 10.3390/brainsci13050780] [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: 04/20/2023] [Revised: 05/02/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023] Open
Abstract
The brain-computer interface (BCI) provides direct communication between human brains and machines, including robots, drones and wheelchairs, without the involvement of peripheral systems. BCI based on electroencephalography (EEG) has been applied in many fields, including aiding people with physical disabilities, rehabilitation, education and entertainment. Among the different EEG-based BCI paradigms, steady-state visual evoked potential (SSVEP)-based BCIs are known for their lower training requirements, high classification accuracy and high information transfer rate (ITR). In this article, a filter bank complex spectrum convolutional neural network (FB-CCNN) was proposed, and it achieved leading classification accuracies of 94.85 ± 6.18% and 80.58 ± 14.43%, respectively, on two open SSVEP datasets. An optimization algorithm named artificial gradient descent (AGD) was also proposed to generate and optimize the hyperparameters of the FB-CCNN. AGD also revealed correlations between different hyperparameters and their corresponding performances. It was experimentally demonstrated that FB-CCNN performed better when the hyperparameters were fixed values rather than channel number-based. In conclusion, a deep learning model named FB-CCNN and a hyperparameter-optimizing algorithm named AGD were proposed and demonstrated to be effective in classifying SSVEP through experiments. The hyperparameter design process and analysis were carried out using AGD, and advice on choosing hyperparameters for deep learning models in classifying SSVEP was provided.
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Affiliation(s)
- Dongcen Xu
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fengzhen Tang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
| | - Yiping Li
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
| | - Qifeng Zhang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
| | - Xisheng Feng
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
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Xu D, Tang F, Li Y, Zhang Q, Feng X. An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey. Brain Sci 2023; 13:brainsci13030483. [PMID: 36979293 PMCID: PMC10046535 DOI: 10.3390/brainsci13030483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/04/2023] [Accepted: 03/10/2023] [Indexed: 03/15/2023] Open
Abstract
The brain–computer interface (BCI), which provides a new way for humans to directly communicate with robots without the involvement of the peripheral nervous system, has recently attracted much attention. Among all the BCI paradigms, BCIs based on steady-state visual evoked potentials (SSVEPs) have the highest information transfer rate (ITR) and the shortest training time. Meanwhile, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, and many researchers have started to apply deep learning to classify SSVEP signals. However, the designs of deep learning models vary drastically. There are many hyper-parameters that influence the performance of the model in an unpredictable way. This study surveyed 31 deep learning models (2011–2023) that were used to classify SSVEP signals and analyzed their design aspects including model input, model structure, performance measure, etc. Most of the studies that were surveyed in this paper were published in 2021 and 2022. This survey is an up-to-date design guide for researchers who are interested in using deep learning models to classify SSVEP signals.
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Affiliation(s)
- Dongcen Xu
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; (D.X.); (F.T.); (Y.L.); (Q.Z.)
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fengzhen Tang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; (D.X.); (F.T.); (Y.L.); (Q.Z.)
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
| | - Yiping Li
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; (D.X.); (F.T.); (Y.L.); (Q.Z.)
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
| | - Qifeng Zhang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; (D.X.); (F.T.); (Y.L.); (Q.Z.)
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
| | - Xisheng Feng
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; (D.X.); (F.T.); (Y.L.); (Q.Z.)
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
- Correspondence:
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Li C, Liu S, Wang Z, Yuan G. Classifying epileptic phase-amplitude coupling in SEEG using complex-valued convolutional neural network. Front Physiol 2023; 13:1085530. [PMID: 36685186 PMCID: PMC9849379 DOI: 10.3389/fphys.2022.1085530] [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: 10/31/2022] [Accepted: 12/20/2022] [Indexed: 01/06/2023] Open
Abstract
EEG phase-amplitude coupling (PAC), the amplitude of high-frequency oscillations modulated by the phase of low-frequency oscillations (LFOs), is a useful biomarker to localize epileptogenic tissue. It is commonly represented in a comodulogram of coupling strength but without coupled phase information. The phase-amplitude coupling is also found in the normal brain, and it is difficult to discriminate pathological phase-amplitude couplings from normal ones. This study proposes a novel approach based on complex-valued phase-amplitude coupling (CV-PAC) for classifying epileptic phase-amplitude coupling. The CV-PAC combines both the coupling strengths and the coupled phases of low-frequency oscillations. The complex-valued convolutional neural network (CV-CNN) is then used to classify epileptic CV-PAC. Stereo-electroencephalography (SEEG) recordings from nine intractable epilepsy patients were analyzed. The leave-one-out cross-validation is performed, and the area-under-curve (AUC) value is used as the indicator of the performance of different measures. Our result shows that the area-under-curve value is .92 for classifying epileptic CV-PAC using CV-CNN. The area-under-curve value decreases to .89, .80, and .88 while using traditional convolutional neural networks, support vector machine, and random forest, respectively. The phases of delta (1-4 Hz) and alpha (8-10 Hz) bands are different between epileptic and normal CV-PAC. The phase information of CV-PAC is important for improving classification performance. The proposed approach of CV-PAC/CV-CNN promises to identify more accurate epileptic brain activities for potential surgical intervention.
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Affiliation(s)
- Chunsheng Li
- Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang, China,*Correspondence: Chunsheng Li,
| | - Shiyue Liu
- Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang, China
| | - Zeyu Wang
- Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang, China,Department of Electrical Engineering and Information Systems, University of Pannonia, Veszprem, Hungary
| | - Guanqian Yuan
- Department of Neurosurgery, General Hospital of Northern Theater Command, Shenyang, China
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