Defining Common Inter-Session and Inter-Subject EEG Channels Using Spatial Selection Method

(1) * Hilman Fauzi Mail (Telkom University, Indonesia)
(2) Tadayasu Komura Mail (Tokyo City University, Japan)
(3) Masaki Kyoso Mail (Tokyo City University, Japan)
(4) Mohd. Ibrahim Shapiai Mail (Universiti Teknologi Malaysia, Malaysia)
(5) Yasmin Mumtaz Mail (Telkom University, Indonesia)
*corresponding author


Redundancy of information on brain signals can lead to reduce brain-computer interface (BCI) performance in applications. To overcome this, EEG channel selection is performed to reduce and/or eliminate a number of channels with irrelevant information. In the previous studies, there is energy calculation methods that have been proposed to perform EEG channel selection to improve BCI performance in classifying the brain command of motor imagery stimulation. In this study, channel selection scheme on motor movement signal will be experimented by using spatial selection method. This study performs the common active channel mechanism that divided into two parts: 1) common active channels between sessions, which known as common Inter-session channels and common active channels. These two techniques can be used by all subjects to interpret motor movement type known as common Inter-subject channels. In order to validate the performance of the proposed framework, CSP (common spatial pattern) is used as a feature extraction method and k-NN with k = 3 as the classification method. The obtained results shows that the proposed channel selection technique is able to choose common active channels in five combination numbers on Inter-sessions and Inter-subjects of the acquired EEG signals. Both types of common active channels are proven to improve BCI performance with an accuracy increase of up to 66%.


BCI; Channel Selection; Energy Calculation; Common Channel; Inter-subject; Inter-session



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S. N. Abdulkader, A. Atia, and M. S. M. Mostafa, “Brain-computer interfacing: Applications and challenges,” Egyptian Informatics Journal, vol. 16, no. 2. pp. 213–230, 2015, doi: 10.1016/j.eij.2015.06.002.

Y. Wang, G. Xu, S. Zhang, A. Luo, M. Li, and C. Han, “EEG signal co-channel interference suppression based on image dimensionality reduction and permutation entropy,” Signal Processing, vol. 134, pp. 113–122, 2017, doi: 10.1016/j.sigpro.2016.11.015.

Z. Qiu, J. Jin, H. K. Lam, Y. Zhang, X. Wang, and A. Cichocki, “Improved SFFS method for channel selection in motor imagery based BCI,” Neurocomputing, vol. 207, pp. 519–527, 2016, doi: 10.1016/j.neucom.2016.05.035.

H. Fauzi, M. I. Shapiai, N. A. Setiawan, J. Jaafar, and M. Mustafa, “Channel selection for common spatial pattern Based on energy calculation of motor imagery EEG signal,” in 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC), Sep. 2017, pp. 33–39, doi: 10.1109/ICCEREC.2017.8226692.

H. Fauzi, M. I. Shapiai, R. Yusof, G. B. Remijn, N. A. Setiawan, and Z. Ibrahim, The design of spatial selection using CUR decomposition to improve common spatial pattern for multi-trial EEG classification, vol. 751. Melaka: Springer, 2017.

N. Mitrovic, M. T. Asif, U. Rasheed, J. Dauwels, and P. Jaillet, “CUR decomposition for compression and compressed sensing of large-scale traffic data,” in IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2013, pp. 1475–1480, doi: 10.1109/ITSC.2013.6728438.

X. Gu et al., “EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications,” IEEE/ACM Trans. Comput. Biol. Bioinforma., 2021, doi: 10.1109/TCBB.2021.3052811.

Y. Cao, C. Gao, H. Yu, L. Zhang, and J. Wang, “Epileptic EEG Channel Selection and Seizure Detection Based on Deep Learning,” Tianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban)/Journal Tianjin Univ. Sci. Technol., 2020, doi: 10.11784/tdxbz201904026.

M. Yıldız and E. Bergil, “The Investigation of Channel Selection Effects on Epileptic Analysis of EEG Signals,” Balk. J. Electr. Comput. Eng., vol. 3, no. 4, pp. 236–241, 2015, doi: 10.17694/bajece.22796.

C. Li, T. Jia, Q. Xu, L. Ji, and Y. Pan, “Brain-computer interface channel-selection strategy based on analysis of event-related desynchronization topography in stroke patients,” J. Healthc. Eng., 2019, doi: 10.1155/2019/3817124.

N. K. Al-Qazzaz, S. H. Md Ali, and S. A. Ahmad, “Differential evolution based channel selection algorithm on EEG signal for early detection of vascular dementia among stroke survivors,” in 2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Proceedings, 2019, doi: 10.1109/IECBES.2018.8626684.

J. L. L. Marcano, M. A. Bell, and A. A. L. Beex, “EEG channel selection for AR model-based ADHD classification,” in 2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings, 2017, doi: 10.1109/SPMB.2016.7846851.

J. Shen et al., “An Optimal Channel Selection for EEG-based Depression Detection via Kernel-Target Alignment,” IEEE J. Biomed. Heal. Informatics, 2020, doi: 10.1109/JBHI.2020.3045718.

Z. M. Wang, S. Y. Hu, and H. Song, “Channel Selection Method for EEG Emotion Recognition Using Normalized Mutual Information,” IEEE Access, 2019, doi: 10.1109/ACCESS.2019.2944273.

G. Zhu, J. Hunter, M. Xi, and Y. Jiang, “Optimal channel selection for detecting alcoholism from EEG based on permutation entropy,” in IWACIII 2017 - 5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, 2017.

T. Alotaiby, F. E. A. El-Samie, S. A. Alshebeili, and I. Ahmad, “A review of channel selection algorithms for EEG signal processing,” EURASIP J. Adv. Signal Process., vol. 2015, no. 1, p. 66, 2015, doi: 10.1186/s13634-015-0251-9.

M. Z. Baig, N. Aslam, and H. P. H. Shum, “Filtering techniques for channel selection in motor imagery EEG applications: a survey,” Artif. Intell. Rev., 2020, doi: 10.1007/s10462-019-09694-8.

H. Varsehi and S. M. P. Firoozabadi, “An EEG channel selection method for motor imagery-based brain-computer interface and neurofeedback using Granger causality,” Neural Networks, 2021, doi: 10.1016/j.neunet.2020.11.002.

H. Fauzi et al., “Energy extraction method for EEG channel selection,” TELKOMNIKA, vol. 17, no. 5, pp. 2561–2571, 2019, doi: 10.12928/TELKOMNIKA.v17i5.12805.

D. Gurve et al., “Subject-specific EEG channel selection using non-negative matrix factorization for lower-limb motor imagery recognition,” J. Neural Eng., 2020, doi: 10.1088/1741-2552/ab4dba.

H. Zhang, X. Zhao, Z. Wu, B. Sun, and T. Li, “Motor imagery recognition with automatic EEG channel selection and deep learning,” J. Neural Eng., 2021, doi: 10.1088/1741-2552/abca16.

P. Arpaia, F. Donnarumma, A. Esposito, and M. Parvis, “Channel Selection for Optimal EEG Measurement in Motor Imagery-Based Brain-Computer Interfaces,” Int. J. Neural Syst., 2021, doi: 10.1142/S0129065721500039.

R. Zhang, Q. Zong, L. Dou, X. Zhao, Y. Tang, and Z. Li, “Hybrid deep neural network using transfer learning for EEG motor imagery decoding,” Biomed. Signal Process. Control, 2021, doi: 10.1016/j.bspc.2020.102144.

F. Fahimi, Z. Zhang, W. B. Goh, T. S. Lee, K. K. Ang, and C. Guan, “Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI,” J. Neural Eng., 2019, doi: 10.1088/1741-2552/aaf3f6.

S. Saha and M. Baumert, “Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain-Computer Interface: A Review,” Frontiers in Computational Neuroscience. 2020, doi: 10.3389/fncom.2019.00087.

S. S. Cohen, “The Inter-Subject Correlation of EEG in Response to Naturalistic Stimuli,” ProQuest Diss. Theses, 2018

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