  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.
