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

2 Citations (Scopus)

Abstract

In this work, we formulate the problem of estimating and selecting task-relevant temporal signal segments from a single 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 BCI 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 conducted experiments with a publicly available big MI dataset and applied our novel mechanism to various recent deep-learning architectures designed for MI classification. Based on the exhaustive experiments, we observed that our proposed method helped achieve statistically significant improvements in performance.

Original languageEnglish
JournalIEEE Transactions on Industrial Informatics
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • Brain modeling
  • Brain-Computer Interface
  • Deep Learning
  • Electroencephalogram
  • Electroencephalography
  • Feature extraction
  • Frequency modulation
  • Informatics
  • Motor Imagery
  • Reinforcement Learning
  • Subject-independent
  • Task analysis
  • Training

ASJC Scopus subject areas

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

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