A Causal Viewpoint 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.
Cite
Text
Barmpas et al. "A Causal Viewpoint on Motor-Imagery Brainwave Decoding." ICLR 2022 Workshops: OSC, 2022.Markdown
[Barmpas et al. "A Causal Viewpoint on Motor-Imagery Brainwave Decoding." ICLR 2022 Workshops: OSC, 2022.](https://mlanthology.org/iclrw/2022/barmpas2022iclrw-causal/)BibTeX
@inproceedings{barmpas2022iclrw-causal,
title = {{A Causal Viewpoint on Motor-Imagery Brainwave Decoding}},
author = {Barmpas, Konstantinos and Panagakis, Yannis and Adamos, Dimitrios and Laskaris, Nikolaos and Zafeiriou, Stefanos},
booktitle = {ICLR 2022 Workshops: OSC},
year = {2022},
url = {https://mlanthology.org/iclrw/2022/barmpas2022iclrw-causal/}
}