A Novel RL-assisted Deep Learning Framework for Task-informative Signals Selection and Classification for Spontaneous BCIs

• URL: http://arxiv.org/abs/2007.00162v1
• Date: Wed, 1 Jul 2020 00:35:41 GMT
• Title: A Novel RL-assisted Deep Learning Framework for Task-informative Signals Selection and Classification for Spontaneous BCIs
• Authors: Wonjun Ko, Eunjin Jeon, and Heung-Il Suk
• Abstract summary: We formulate the problem of estimating and selecting task-relevant temporal signal segments from a single EEG trial. We propose a novel reinforcement-learning mechanism that can be combined with the existing deep-learning based BCI methods.
• Score: 2.299749220980997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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