A Strong and Simple Deep Learning Baseline for BCI MI Decoding

We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a simple and performing baseline to compare to, using only very standard ingredients from the literature. We evaluate its performance on four EEG Motor Im...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Yassine El Ouahidi, Gripon, Vincent, Bastien Pasdeloup, Ghaith Bouallegue, Farrugia, Nicolas, Lioi, Giulia
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 25.01.2024
Subjects
Artificial neural networks
Decoding
Deep learning
Imagery
Ingredients
Knowledge management
Machine learning
Online AccessGet full text

Cover

More Information
Summary:We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a simple and performing baseline to compare to, using only very standard ingredients from the literature. We evaluate its performance on four EEG Motor Imagery datasets, including simulated online setups, and compare it to recent Deep Learning and Machine Learning approaches. EEG-SimpleConv is at least as good or far more efficient than other approaches, showing strong knowledge-transfer capabilities across subjects, at the cost of a low inference time. We advocate that using off-the-shelf ingredients rather than coming with ad-hoc solutions can significantly help the adoption of Deep Learning approaches for BCI. We make the code of the models and the experiments accessible.
ISSN:2331-8422