EEG channel selection algorithm based on Reinforcement Learning
Document Type
Conference Proceeding
Publication Date
1-1-2022
Abstract
Multichannel EEG is generally used to collect brain activities from various locations across the brain. However, BCIs using lesser channels will be more convenient for subjects. What's more, information acquired from adjacent channels is usually inter-correlated or irrelevant to the task. And some channels are noisy. This paper proposes a novel channel selection algorithm based on reinforcement learning. It can adaptively transform the full-channel EEG data to the optimal-channel-number EEG format conditioned on different input trials to make a trade-off between brain decoding accuracy and efficiency. Experimen-tal results showed that the proposed model can improve the classification accuracy by 2% 6% compared to channel set C3,C4,Cz.
Identifier
85146919112 (Scopus)
ISBN
[9781665472432]
Publication Title
Icnsc 2022 Proceedings of 2022 IEEE International Conference on Networking Sensing and Control Autonomous Intelligent Systems
External Full Text Location
https://doi.org/10.1109/ICNSC55942.2022.10004161
Grant
62171323
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Jin, Yingxin; Shang, Shaohua; Tang, Liwei; He, Lianzhua; and Zhou, Meng Chu, "EEG channel selection algorithm based on Reinforcement Learning" (2022). Faculty Publications. 3405.
https://digitalcommons.njit.edu/fac_pubs/3405
