Continual Learning for EEG based Brain Computer Interfaces

Param Rajpura, Pankaj Pandey, Krishna Miyapuram

Keywords: Continual Learning, Brain Computer Interface, EEG

TL;DR: Evaluating popular continual learning strategies for BCI using EEG for motor movements and motor imagery.

Abstract:

A healthy human brain manifests a high variance in signals captured from different modalities like EEG, MEG, and fMRI during ongoing activity. Further, there is brain signal variability exhibited at the user level. The study of within-subject and cross-subject variance is essential to design general Brain-Computer Interfaces (BCI) that help interpret these signals into intended outcomes. We propose that these variations can be studied under the umbrella of Continual Learning (CL). We performed an empirical evaluation to understand the impact of CL strategies on the benchmark dataset. Our findings in within-subject and cross-subject scenarios suggest that CL strategies can outperform offline learning and build robust models for BCI applications.
In the cross-subject scenario, CL can lead to learning invariant subject representation when transferring knowledge from one subject to another. In the within-subject scenario, CL can enhance performance when transferring knowledge from one session to another.