A review on electroencephalogram based channel selection_Journal of Biomedical Engineering

Authors：

LI Xiangzhe ¹ ,  WANG Dan ¹ , ZHANG Baiwen ² , FAN Chaojie ³ , CHEN Jiaming ¹ , XU Meng ¹ ,  CHEN Yuanfang ⁴

1. Faculty of information Technology, Beijing University of Technology, Beijing 100124, P.R. China;
2. Institute of Information and Artificial Intelligence Technology, Beijing Academy of Science and Technology, Beijing 100089, P.R. China;
3. School of Transportation Engineering, Central South University, Changsha 410075, P.R. China;
4. Beijing Institute of Mechanical Equipment, Beijing 100854, P.R. China;

Corresponding author：

WANG Dan, Email: wangdan@bjut.edu.cn

Keywords：

Electroencephalogram signal decoding; Channel selection; Brain-computer interface

DOI：

10.7507/1001-5515.202308034

Video：

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The electroencephalogram (EEG) signal is the key signal carrier of the brain-computer interface (BCI) system. The EEG data collected by the whole-brain electrode arrangement is conducive to obtaining higher information representation. Personalized electrode layout, while ensuring the accuracy of EEG signal decoding, can also shorten the calibration time of BCI and has become an important research direction. This paper reviews the EEG signal channel selection methods in recent years, conducts a comparative analysis of the combined effects of different channel selection methods and different classification algorithms, obtains the commonly used channel combinations in motor imagery, P300 and other paradigms in BCI, and explains the application scenarios of the channel selection method in different paradigms are discussed, in order to provide stronger support for a more accurate and portable BCI system.

Citation： LI Xiangzhe, WANG Dan, ZHANG Baiwen, FAN Chaojie, CHEN Jiaming, XU Meng, CHEN Yuanfang. A review on electroencephalogram based channel selection. Journal of Biomedical Engineering, 2024, 41(2): 398-405. doi: 10.7507/1001-5515.202308034 Copy

1.	Jin J, Sun H, Daly I, et al. A novel classification framework using the graph representations of electroencephalogram for motor imagery based brain-computer interface. IEEE Trans Neural Syst Rehab Eng, 2022, 30: 20-29.
2.	Li J W, Barma S, Mak P U, et al. Single-channel selection for EEG-based emotion recognition using brain rhythm sequencing. IEEE J Biomed Health Inform, 2022, 26(6): 2493-2503.
3.	Bruner E, Battaglia-Mayer A, Caminiti R. The parietal lobe evolution and the emergence of material culture in the human genus. Brain Struct Funct, 2023, 228(1): 145-167.
4.	谢士遥, 汤佳贝, 蔡雨, 等. 脑电BCI系统的软硬件开发平台发展现状. 电子测量与仪器学报, 2022, 36(6): 1-12.
5.	罗建功, 丁鹏, 龚安民, 等. 脑机接口技术的应用、产业转化和商业价值. 生物医学工程学杂志, 2022, 39(2): 405-415.
6.	Varsehi H, Firoozabadi S M P. An EEG channel selection method for motor imagery based brain-computer interface and neurofeedback using Granger causality. Neural Netw. 2021, 133: 193-206.
7.	Lal T N, Schroder M, Hinterberger T, et al. Support vector channel selection in BCI. IEEE Trans Biomed Eng, 2004, 51(6): 1003-1010.
8.	Tiwari A. A logistic binary Jaya optimization-based channel selection scheme for motor-imagery classification in brain-computer interface. Expert Syst Appl, 2023, 223: 119921.
9.	Dhiman R. Electroencephalogram channel selection based on pearson correlation coefficient for motor imagery-brain-computer interface. Measurement: Sensors, 2023, 25: 100616.
10.	Jin J, Liu C, Daly I, et al. Bispectrum-based channel selection for motor imagery based brain-computer interfacing. IEEE Trans Neural Syst Rehab Eng, 2020, 28(10): 2153-2163.
11.	Cox D D, Savoy R L. Functional magnetic resonance imaging (fMRI) "brain reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage, 2003, 19(2): 261-270.
12.	Šverko Z, Vrankić M, Vlahinić S, et al. Complex Pearson correlation coefficient for EEG connectivity analysis. Sensors, 2022, 22(4): 1477.
13.	Jin J, Miao Y, Daly I, et al. Correlation-based channel selection and regularized feature optimization for MI-based BCI. Neural Netw, 2019, 118: 262-270.
14.	孟明, 胡家豪, 高云园, 等. 结合互信息通道选择与混合深度神经网络的脑电情感识别方法. 传感技术学报, 2021, 34(8): 7.
15.	Mahamune R, Laskar S H. An automatic channel selection method based on the standard deviation of wavelet coefficients for motor imagery based brain–computer interfacing. Int J Imaging Syst Technol, 2023, 33(2): 714-728.
16.	Mu W, Wang J, Wang L, et al. A channel selection method for motor imagery EEG based on Fisher score of OVR-CSP//2023 11th International Winter Conference on Brain-Computer Interface (BCI). Gangwon: IEEE, 2023: 1-4.
17.	Ramoser H, Müller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehab Eng, 2000, 8(4): 441-446.
18.	Qi Y, Ding F, Xu F, et al. Channel and feature selection for a motor imagery-based BCI system using multilevel particle swarm optimization. Comput Intell Neurosci 2020, 2020(11): 8890477.
19.	Kennedy J, Eberhart R. Particle swarm optimization//Proceedings of ICNN'95-International Conference on Neural Networks. Perth: IEEE, 1995, 4: 1942-1948.
20.	Padfield N, Ren J, Murray P, et al. Sparse learning of band power features with genetic channel selection for effective classification of EEG signals. Neurocomputing, 2021, 463: 566-579.
21.	Eid M M, Alassery F, Ibrahim A, et al. Metaheuristic optimization algorithm for signals classification of electroencephalography channels. Comput Materials Contin, 2022, 71(3): 4627-4641.
22.	Sun H, Jin J, Kong W, et al. Novel channel selection method based on position priori weighted permutation entropy and binary gravity search algorithm. Cogn Neurodyn, 2021, 15(1): 141-156.
23.	Martínez-Cagigal V, Santamaría-Vázquez E, Hornero R. Brain–computer interface channel selection optimization using meta-heuristics and evolutionary algorithms. Appl Soft Comput, 2022, 115: 108176.
24.	Xu M, Chen Y, Wang D, et al. Multi-objective optimization approach for channel selection and cross-subject generalization in RSVP-based BCIs. J Neural Eng, 2021, 18(4): 046076.
25.	Tiwari A, Chaturvedi A. Automatic EEG channel selection for multiclass brain-computer interface classification using multi-objective improved firefly algorithm. Multimed Tools Appl, 2023, 82(4): 5405-5433.
26.	Shi B, Yue Z, Yin S, et al. Adaptive binary multi-objective harmony search algorithm for channel selection and cross-subject generalization in motor imagery-based BCI. J Neural Eng, 2022, 19(4): 046022.
27.	Sun B, Liu Z, Wu Z, et al. Graph convolution neural network based end-to-end channel selection and classification for motor imagery brain-computer interfaces. IEEE Trans Industr Inform, 2022, 19: 9314-9324.
28.	Strypsteen T, Bertrand A. End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax. J Neural Eng, 2021, 18(4): 0460a9.
29.	Yan M, Lv Z, Sun W, et al. An improved common spatial pattern combined with channel-selection strategy for electroencephalography-based emotion recognition. Med Eng Phys, 2020, 83: 130-141.
30.	Zhao X, Jin J, Xu R, et al. A regional smoothing block sparse Bayesian learning method with temporal correlation for channel selection in P300 speller. Front Hum Neurosci, 2022, 16: 875851.
31.	Topic A, Russo M, Stella M, et al. Emotion recognition using a reduced set of EEG channels based on holographic feature maps. Sensors, 2022, 22(9): 3248.
32.	Zhang J, Wang M. A survey on robots controlled by motor imagery brain-computer interfaces. Cogn Robot, 2021, 1: 12-24.
33.	Ortiz M, de la Ossa L, Juan J, et al. An EEG database for the cognitive assessment of motor imagery during walking with a lower-limb exoskeleton. Sci Data, 2023, 10(1): 343.
34.	Liu Y, Wang Z, Huang S, et al. EEG characteristic investigation of the sixth-finger motor imagery and optimal channel selection for classification. J Neural Eng, 2022, 19(1): 016001.
35.	Zeng Y, Wu Q, Yang K, et al. EEG-based identity authentication framework using face rapid serial visual presentation with optimized channels. Sensors, 2018, 19(1): 6.
36.	Wang K, Zhai D H, Xiong Y, et al. An MVMD-CCA recognition algorithm in SSVEP-based BCI and its application in robot control. IEEE Trans Neural Netw Learni Syst, 2021, 33(5): 2159-2167.
37.	Meng L, Jin J, Wang X. A comparison of three electrode channels selection methods applied to SSVEP BCI//2011 4th International Conference on Biomedical Engineering and Informatics (BMEI). Shanghai: IEEE, 2011, 1: 584-587.
38.	Ghembaza F, Djebbari A. A robust dynamic EEG channel selection using time–frequency extended Renyi entropy//2022 7th International Conference on Image and Signal Processing and their Applications (ISPA). Mostaganem: IEEE, 2022: 1-8.
39.	汝彦冬, 李金宝, 吕兴凤, 等. 基于脑电通道动态选择方法的癫痫检测. 仪器仪表学报, 2023(2): 180-188.

1. Jin J, Sun H, Daly I, et al. A novel classification framework using the graph representations of electroencephalogram for motor imagery based brain-computer interface. IEEE Trans Neural Syst Rehab Eng, 2022, 30: 20-29.
2. Li J W, Barma S, Mak P U, et al. Single-channel selection for EEG-based emotion recognition using brain rhythm sequencing. IEEE J Biomed Health Inform, 2022, 26(6): 2493-2503.
3. Bruner E, Battaglia-Mayer A, Caminiti R. The parietal lobe evolution and the emergence of material culture in the human genus. Brain Struct Funct, 2023, 228(1): 145-167.
4. 谢士遥, 汤佳贝, 蔡雨, 等. 脑电BCI系统的软硬件开发平台发展现状. 电子测量与仪器学报, 2022, 36(6): 1-12.
5. 罗建功, 丁鹏, 龚安民, 等. 脑机接口技术的应用、产业转化和商业价值. 生物医学工程学杂志, 2022, 39(2): 405-415.
6. Varsehi H, Firoozabadi S M P. An EEG channel selection method for motor imagery based brain-computer interface and neurofeedback using Granger causality. Neural Netw. 2021, 133: 193-206.
7. Lal T N, Schroder M, Hinterberger T, et al. Support vector channel selection in BCI. IEEE Trans Biomed Eng, 2004, 51(6): 1003-1010.
8. Tiwari A. A logistic binary Jaya optimization-based channel selection scheme for motor-imagery classification in brain-computer interface. Expert Syst Appl, 2023, 223: 119921.
9. Dhiman R. Electroencephalogram channel selection based on pearson correlation coefficient for motor imagery-brain-computer interface. Measurement: Sensors, 2023, 25: 100616.
10. Jin J, Liu C, Daly I, et al. Bispectrum-based channel selection for motor imagery based brain-computer interfacing. IEEE Trans Neural Syst Rehab Eng, 2020, 28(10): 2153-2163.
11. Cox D D, Savoy R L. Functional magnetic resonance imaging (fMRI) "brain reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage, 2003, 19(2): 261-270.
12. Šverko Z, Vrankić M, Vlahinić S, et al. Complex Pearson correlation coefficient for EEG connectivity analysis. Sensors, 2022, 22(4): 1477.
13. Jin J, Miao Y, Daly I, et al. Correlation-based channel selection and regularized feature optimization for MI-based BCI. Neural Netw, 2019, 118: 262-270.
14. 孟明, 胡家豪, 高云园, 等. 结合互信息通道选择与混合深度神经网络的脑电情感识别方法. 传感技术学报, 2021, 34(8): 7.
15. Mahamune R, Laskar S H. An automatic channel selection method based on the standard deviation of wavelet coefficients for motor imagery based brain–computer interfacing. Int J Imaging Syst Technol, 2023, 33(2): 714-728.
16. Mu W, Wang J, Wang L, et al. A channel selection method for motor imagery EEG based on Fisher score of OVR-CSP//2023 11th International Winter Conference on Brain-Computer Interface (BCI). Gangwon: IEEE, 2023: 1-4.
17. Ramoser H, Müller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehab Eng, 2000, 8(4): 441-446.
18. Qi Y, Ding F, Xu F, et al. Channel and feature selection for a motor imagery-based BCI system using multilevel particle swarm optimization. Comput Intell Neurosci 2020, 2020(11): 8890477.
19. Kennedy J, Eberhart R. Particle swarm optimization//Proceedings of ICNN'95-International Conference on Neural Networks. Perth: IEEE, 1995, 4: 1942-1948.
20. Padfield N, Ren J, Murray P, et al. Sparse learning of band power features with genetic channel selection for effective classification of EEG signals. Neurocomputing, 2021, 463: 566-579.
21. Eid M M, Alassery F, Ibrahim A, et al. Metaheuristic optimization algorithm for signals classification of electroencephalography channels. Comput Materials Contin, 2022, 71(3): 4627-4641.
22. Sun H, Jin J, Kong W, et al. Novel channel selection method based on position priori weighted permutation entropy and binary gravity search algorithm. Cogn Neurodyn, 2021, 15(1): 141-156.
23. Martínez-Cagigal V, Santamaría-Vázquez E, Hornero R. Brain–computer interface channel selection optimization using meta-heuristics and evolutionary algorithms. Appl Soft Comput, 2022, 115: 108176.
24. Xu M, Chen Y, Wang D, et al. Multi-objective optimization approach for channel selection and cross-subject generalization in RSVP-based BCIs. J Neural Eng, 2021, 18(4): 046076.
25. Tiwari A, Chaturvedi A. Automatic EEG channel selection for multiclass brain-computer interface classification using multi-objective improved firefly algorithm. Multimed Tools Appl, 2023, 82(4): 5405-5433.
26. Shi B, Yue Z, Yin S, et al. Adaptive binary multi-objective harmony search algorithm for channel selection and cross-subject generalization in motor imagery-based BCI. J Neural Eng, 2022, 19(4): 046022.
27. Sun B, Liu Z, Wu Z, et al. Graph convolution neural network based end-to-end channel selection and classification for motor imagery brain-computer interfaces. IEEE Trans Industr Inform, 2022, 19: 9314-9324.
28. Strypsteen T, Bertrand A. End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax. J Neural Eng, 2021, 18(4): 0460a9.
29. Yan M, Lv Z, Sun W, et al. An improved common spatial pattern combined with channel-selection strategy for electroencephalography-based emotion recognition. Med Eng Phys, 2020, 83: 130-141.
30. Zhao X, Jin J, Xu R, et al. A regional smoothing block sparse Bayesian learning method with temporal correlation for channel selection in P300 speller. Front Hum Neurosci, 2022, 16: 875851.
31. Topic A, Russo M, Stella M, et al. Emotion recognition using a reduced set of EEG channels based on holographic feature maps. Sensors, 2022, 22(9): 3248.
32. Zhang J, Wang M. A survey on robots controlled by motor imagery brain-computer interfaces. Cogn Robot, 2021, 1: 12-24.
33. Ortiz M, de la Ossa L, Juan J, et al. An EEG database for the cognitive assessment of motor imagery during walking with a lower-limb exoskeleton. Sci Data, 2023, 10(1): 343.
34. Liu Y, Wang Z, Huang S, et al. EEG characteristic investigation of the sixth-finger motor imagery and optimal channel selection for classification. J Neural Eng, 2022, 19(1): 016001.
35. Zeng Y, Wu Q, Yang K, et al. EEG-based identity authentication framework using face rapid serial visual presentation with optimized channels. Sensors, 2018, 19(1): 6.
36. Wang K, Zhai D H, Xiong Y, et al. An MVMD-CCA recognition algorithm in SSVEP-based BCI and its application in robot control. IEEE Trans Neural Netw Learni Syst, 2021, 33(5): 2159-2167.
37. Meng L, Jin J, Wang X. A comparison of three electrode channels selection methods applied to SSVEP BCI//2011 4th International Conference on Biomedical Engineering and Informatics (BMEI). Shanghai: IEEE, 2011, 1: 584-587.
38. Ghembaza F, Djebbari A. A robust dynamic EEG channel selection using time–frequency extended Renyi entropy//2022 7th International Conference on Image and Signal Processing and their Applications (ISPA). Mostaganem: IEEE, 2022: 1-8.
39. 汝彦冬, 李金宝, 吕兴凤, 等. 基于脑电通道动态选择方法的癫痫检测. 仪器仪表学报, 2023(2): 180-188.

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A review on electroencephalogram based channel selection

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