A Novel RL-Assisted Deep Learning Framework for Task-Informative Signals Selection and Classification for Spontaneous BCIs

Wonjun Ko, Eunjin Jeon, Heung Il Suk

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this article, we formulate the problem of estimating and selecting task-relevant temporal signal segments from a single electroencephalogram (EEG) trial in the form of a Markov decision process and propose a novel reinforcement-learning mechanism that can be combined with the existing deep-learning-based brain-computer interface methods. To be specific, we devise an actor-critic network such that an agent can determine which timepoints need to be used (informative) or discarded (uninformative) in composing the intention-related features in a given trial, and thus enhancing the intention identification performance. To validate the effectiveness of our proposed method, we conduct experiments with a publicly available big motor imagery (MI) dataset and apply our novel mechanism to various recent deep-learning architectures designed for MI classification. Based on the exhaustive experiments, we observe that our proposed method helped achieve statistically significant improvements in performance.

Original languageEnglish
Pages (from-to)1873-1882
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Volume18
Issue number3
DOIs
Publication statusPublished - 2022 Mar 1

Keywords

  • Brain-computer interface (BCI)
  • deep learning
  • electroencephalogram (EEG)
  • motor imagery
  • reinforcement learning (RL)
  • subject independent

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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