EEG channel selection algorithm based on Reinforcement Learning

Yingxin Jin, Shaohua Shang, Liwei Tang, Lianzhua He, Meng Chu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationICNSC 2022 - Proceedings of 2022 IEEE International Conference on Networking, Sensing and Control
Subtitle of host publicationAutonomous Intelligent Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665472432
DOIs
StatePublished - 2022
Event19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022 - Shanghai, China
Duration: Dec 15 2022Dec 18 2022

Publication series

NameICNSC 2022 - Proceedings of 2022 IEEE International Conference on Networking, Sensing and Control: Autonomous Intelligent Systems

Conference

Conference19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022
Country/TerritoryChina
CityShanghai
Period12/15/2212/18/22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Control and Optimization

Keywords

  • EEG
  • channel selection
  • reinforcement learning

Fingerprint

Dive into the research topics of 'EEG channel selection algorithm based on Reinforcement Learning'. Together they form a unique fingerprint.

Cite this