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

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 achiev...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2024-05, Vol.54 (5), p.3084-3096
Main Authors: Shin, Dong-Hee, Son, Young-Han, Kim, Jun-Mo, Ahn, Hee-Jun, Seo, Jun-Ho, Ji, Chang-Hoon, Han, Ji-Wung, Lee, Byung-Jun, Won, Dong-Ok, Kam, Tae-Eui
Format: Article
Language:English
Subjects:
Brain–computer interface (BCI)
Computer networks
Data mining
Deep learning
Electroencephalography
Feature extraction
Feature selection
Image classification
Machine learning
motor imagery (MI)
multiagent reinforcement learning (RL)
Multiagent systems
Reinforcement learning
Robots
Task analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2024.3355101