A CAUSAL VIEWPOINT ON MOTOR-IMAGERY BRAINWAVE DECODING

Konstantinos Barmpas, Yannis Panagakis, Dimitrios Adamos, Nikolaos Laskaris, Stefanos Zafeiriou

Keywords: Causality, Brainwaves, Brain-Computer Interfaces, Dynamic Convolution

TL;DR: Causality on Motor-Imagery Brainwave Decoding

Abstract: In this work, we employ causal reasoning to breakdown and analyze important challenges of the decoding of Motor-Imagery (MI) electroencephalography (EEG) signals. Furthermore, we present a framework consisting of dynamic convolutions, that address one of the issues that arises through this causal investigation, namely the subject distribution shift (or inter-subject variability). Using a publicly available MI dataset, we demonstrate increased cross-subject performance in two different MI tasks for four well-established deep architectures.