MARS: Multiagent Reinforcement Learning for Spatial - Spectral and Temporal Feature Selection in EEG-Based BCI

Dong Hee Shin, Young Han Son, Jun Mo Kim, Hee Jun Ahn, Jun Ho Seo, Chang Hoon Ji, Ji Wung Han, Byung Jun Lee, Dong Ok Won, Tae Eui Kam

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In recent years, deep learning methods have shown promising capabilities for extracting informative and discriminative features from electroencephalography (EEG) data. However, several studies have reported that the feature selection process followed by feature extraction can be beneficial to achieve further performance improvement. Even though a recent work achieved promising results by using the single-agent reinforcement learning (RL)-based framework to select task-relevant features in the temporal domain, it still failed to consider other significant features in the spatial-spectral domain. To overcome such limitations, we propose a cooperative multiagent RL-based framework (MARS) that performs feature selection in both the spatial-spectral and temporal domains simultaneously for a motor imagery (MI)-EEG classification task. In this framework, we enable our RL agents to collaborate with each other as a team to solve a complex multiobjective feature selection problem. Furthermore, we adopt a counterfactual advantage function to overcome the free-rider problem, which is associated with the credit assignment issue in multiagent cases. To assess the MARS framework, we conduct extensive experiments with two public MI datasets under subject-dependent and subject-independent scenarios and we apply the MARS to different backbone networks. The experimental results demonstrate that our MARS outperforms other competing methods in terms of mean accuracy and achieves statistically significant improvements.

Original languageEnglish
Pages (from-to)3084-3096
Number of pages13
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number5
Publication statusPublished - 2024 May 1

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Brain-computer interface (BCI)
  • feature selection
  • motor imagery (MI)
  • multiagent reinforcement learning (RL)

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications


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