Unsupervised Adaptive Deep Learning Method For BCI Motor Imagery Decoding

In the context of Brain-Computer Interfaces, we propose an adaptive method that reaches offline performance level while being usable online without requiring supervision. Interestingly, our method does not require retraining the model, as it consists in using a frozen efficient deep learning backbon...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Yassine El Ouahidi, Lioi, Giulia, Farrugia, Nicolas, Bastien Pasdeloup, Gripon, Vincent
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 15.03.2024
Subjects
Decoding
Deep learning
Human-computer interface
Imagery
Online AccessGet full text

Cover

More Information
Summary:In the context of Brain-Computer Interfaces, we propose an adaptive method that reaches offline performance level while being usable online without requiring supervision. Interestingly, our method does not require retraining the model, as it consists in using a frozen efficient deep learning backbone while continuously realigning data, both at input and latent spaces, based on streaming observations. We demonstrate its efficiency for Motor Imagery brain decoding from electroencephalography data, considering challenging cross-subject scenarios. For reproducibility, we share the code of our experiments.
ISSN:2331-8422