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Luo Y, Mu W, Wang L, Wang J, Wang P, Gan Z, Zhang L, Kang X. An EEG channel selection method for motor imagery based on Fisher score and local optimization. J Neural Eng 2024; 21:036030. [PMID: 38842111 DOI: 10.1088/1741-2552/ad504a] [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: 10/25/2023] [Accepted: 05/24/2024] [Indexed: 06/07/2024]
Abstract
Objective. Multi-channel electroencephalogram (EEG) technology in brain-computer interface (BCI) research offers the advantage of enhanced spatial resolution and system performance. However, this also implies that more time is needed in the data processing stage, which is not conducive to the rapid response of BCI. Hence, it is a necessary and challenging task to reduce the number of EEG channels while maintaining decoding effectiveness.Approach. In this paper, we propose a local optimization method based on the Fisher score for within-subject EEG channel selection. Initially, we extract the common spatial pattern characteristics of EEG signals in different bands, calculate Fisher scores for each channel based on these characteristics, and rank them accordingly. Subsequently, we employ a local optimization method to finalize the channel selection.Main results. On the BCI Competition IV Dataset IIa, our method selects an average of 11 channels across four bands, achieving an average accuracy of 79.37%. This represents a 6.52% improvement compared to using the full set of 22 channels. On our self-collected dataset, our method similarly achieves a significant improvement of 24.20% with less than half of the channels, resulting in an average accuracy of 76.95%.Significance. This research explores the importance of channel combinations in channel selection tasks and reveals that appropriately combining channels can further enhance the quality of channel selection. The results indicate that the model selected a small number of channels with higher accuracy in two-class motor imagery EEG classification tasks. Additionally, it improves the portability of BCI systems through channel selection and combinations, offering the potential for the development of portable BCI systems.
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Affiliation(s)
- Yangjie Luo
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Wei Mu
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Lu Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Junkongshuai Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Pengchao Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Zhongxue Gan
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
- Ji Hua Laboratory, Foshan, People's Republic of China
| | - Lihua Zhang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
- Ji Hua Laboratory, Foshan, People's Republic of China
| | - Xiaoyang Kang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
- Ji Hua Laboratory, Foshan, People's Republic of China
- Yiwu Research Institute of Fudan University, Yiwu City, People's Republic of China
- Research Center for Intelligent Sensing, Zhejiang Lab, Hangzhou, People's Republic of China
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Yan S, Hu Y, Zhang R, Qi D, Hu Y, Yao D, Shi L, Zhang L. Multilayer network-based channel selection for motor imagery brain-computer interface. J Neural Eng 2024; 21:016029. [PMID: 38295419 DOI: 10.1088/1741-2552/ad2496] [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: 08/17/2023] [Accepted: 01/31/2024] [Indexed: 02/02/2024]
Abstract
Objective. The number of electrode channels in a motor imagery-based brain-computer interface (MI-BCI) system influences not only its decoding performance, but also its convenience for use in applications. Although many channel selection methods have been proposed in the literature, they are usually based on the univariate features of a single channel. This leads to a loss of the interaction between channels and the exchange of information between networks operating at different frequency bands.Approach. We integrate brain networks containing four frequency bands into a multilayer network framework and propose a multilayer network-based channel selection (MNCS) method for MI-BCI systems. A graph learning-based method is used to estimate the multilayer network from electroencephalogram (EEG) data that are filtered by multiple frequency bands. The multilayer participation coefficient of the multilayer network is then computed to select EEG channels that do not contain redundant information. Furthermore, the common spatial pattern (CSP) method is used to extract effective features. Finally, a support vector machine classifier with a linear kernel is trained to accurately identify MI tasks.Main results. We used three publicly available datasets from the BCI Competition containing data on 12 healthy subjects and one dataset containing data on 15 stroke patients to validate the effectiveness of our proposed method. The results showed that the proposed MNCS method outperforms all channels (85.8% vs. 93.1%, 84.4% vs. 89.0%, 71.7% vs. 79.4%, and 72.7% vs. 84.0%). Moreover, it achieved significantly higher decoding accuracies on MI-BCI systems than state-of-the-art methods (pairedt-tests,p< 0.05).Significance. The experimental results showed that the proposed MNCS method can select appropriate channels to improve the decoding performance as well as the convenience of the application of MI-BCI systems.
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Affiliation(s)
- Shaoting Yan
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, People's Republic of China
| | - Yuxia Hu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, People's Republic of China
| | - Rui Zhang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, People's Republic of China
| | - Daowei Qi
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
| | - Yubo Hu
- The No.3 Provincial People's Hospital of Henan Province, Zhengzhou, People's Republic of China
| | - Dezhong Yao
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
| | - Li Shi
- Department of Automation, Tsinghua University, Beijing, People's Republic of China
- Beijing National Research Center for Information Science and Technology, Beijing, People's Republic of China
| | - Lipeng Zhang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, People's Republic of China
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Wu Z, Tang X, Wu J, Huang J, Shen J, Hong H. Portable deep-learning decoder for motor imaginary EEG signals based on a novel compact convolutional neural network incorporating spatial-attention mechanism. Med Biol Eng Comput 2023; 61:2391-2404. [PMID: 37095297 DOI: 10.1007/s11517-023-02840-z] [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: 10/10/2022] [Accepted: 04/13/2023] [Indexed: 04/26/2023]
Abstract
Due to high computational requirements, deep-learning decoders for motor imaginary (MI) electroencephalography (EEG) signals are usually implemented on bulky and heavy computing devices that are inconvenient for physical actions. To date, the application of deep-learning techniques in independent portable brain-computer-interface (BCI) devices has not been extensively explored. In this study, we proposed a high-accuracy MI EEG decoder by incorporating spatial-attention mechanism into convolution neural network (CNN), and deployed it on fully integrated single-chip microcontroller unit (MCU). After the CNN model was trained on workstation computer using GigaDB MI datasets (52 subjects), its parameters were then extracted and converted to build deep-learning architecture interpreter on MCU. For comparison, EEG-Inception model was also trained using the same dataset, and was deployed on MCU. The results indicate that our deep-learning model can independently decode imaginary left-/right-hand motions. The mean accuracy of the proposed compact CNN reaches 96.75 ± 2.41% (8 channels: Frontocentral3 (FC3), FC4, Central1 (C1), C2, Central-Parietal1 (CP1), CP2, C3, and C4), versus 76.96 ± 19.08% of EEG-Inception (6 channels: FC3, FC4, C1, C2, CP1, and CP2). To the best of our knowledge, this is the first portable deep-learning decoder for MI EEG signals. The findings demonstrate high-accuracy deep-learning decoding of MI EEG in a portable mode, which has great implications for hand-disabled patients. Our portable system can be used for developing artificial-intelligent wearable BCI devices, as it is less computationally expensive and convenient for real-life application.
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Affiliation(s)
- Zhanxiong Wu
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China.
| | - Xudong Tang
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Jinhui Wu
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Jiye Huang
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Jian Shen
- Neurosurgery Department, The First Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, China
| | - Hui Hong
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
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Abenna S, Nahid M, Bouyghf H, Ouacha B. An enhanced motor imagery EEG signals prediction system in real-time based on delta rhythm. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Abenna S, Nahid M, Bouyghf H, Ouacha B. EEG-based BCI: A novel improvement for EEG signals classification based on real-time preprocessing. Comput Biol Med 2022; 148:105931. [DOI: 10.1016/j.compbiomed.2022.105931] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/12/2022] [Accepted: 07/30/2022] [Indexed: 11/27/2022]
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Jia X, Song Y, Yang L, Xie L. Joint spatial and temporal features extraction for multi-classification of motor imagery EEG. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103247] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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An improved version of local activities estimation to enhance motor imagery classification. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102485] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Rashid M, Bari BS, Hasan MJ, Razman MAM, Musa RM, Ab Nasir AF, P.P. Abdul Majeed A. The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN. PeerJ Comput Sci 2021; 7:e374. [PMID: 33817022 PMCID: PMC7959631 DOI: 10.7717/peerj-cs.374] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 01/06/2021] [Indexed: 05/27/2023]
Abstract
Brain-computer interface (BCI) is a viable alternative communication strategy for patients of neurological disorders as it facilitates the translation of human intent into device commands. The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. More often than not, high dimensional feature set contains redundant features that may degrade a given classifier's performance. In the present investigation, an ensemble learning-based classification algorithm, namely random subspace k-nearest neighbour (k-NN) has been proposed to classify the motor imagery (MI) data. The common spatial pattern (CSP) has been applied to extract the features from the MI response, and the effectiveness of random forest (RF)-based feature selection algorithm has also been investigated. In order to evaluate the efficacy of the proposed method, an experimental study has been implemented using four publicly available MI dataset (BCI Competition III dataset 1 (data-1), dataset IIIA (data-2), dataset IVA (data-3) and BCI Competition IV dataset II (data-4)). It was shown that the ensemble-based random subspace k-NN approach achieved the superior classification accuracy (CA) of 99.21%, 93.19%, 93.57% and 90.32% for data-1, data-2, data-3 and data-4, respectively against other models evaluated, namely linear discriminant analysis, support vector machine, random forest, Naïve Bayes and the conventional k-NN. In comparison with other classification approaches reported in the recent studies, the proposed method enhanced the accuracy by 2.09% for data-1, 1.29% for data-2, 4.95% for data-3 and 5.71% for data-4, respectively. Moreover, it is worth highlighting that the RF feature selection technique employed in the present study was able to significantly reduce the feature dimension without compromising the overall CA. The outcome from the present study implies that the proposed method may significantly enhance the accuracy of MI data classification.
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Affiliation(s)
- Mamunur Rashid
- Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Bifta Sama Bari
- Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Md Jahid Hasan
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Mohd Azraai Mohd Razman
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Rabiu Muazu Musa
- Centre for Fundamental and Continuing Education, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
| | - Ahmad Fakhri Ab Nasir
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
- Centre for Software Development & Integrated Computing, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Anwar P.P. Abdul Majeed
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
- Centre for Software Development & Integrated Computing, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
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