  The integration of Deep Learning (DL) algorithms on brain signal analysis is
still in its nascent stages compared to their success in fields like Computer
Vision, especially in Brain-Computer Interface (BCI), where the brain activity
is decoded to control external devices without requiring muscle control.
Electroencephalography (EEG) is a widely adopted choice for designing BCI
systems due to its non-invasive and cost-effective nature and excellent
temporal resolution. Still, it comes at the expense of limited training data,
poor signal-to-noise, and a large variability across and within-subject
recordings. Finally, setting up a BCI system with many electrodes takes a long
time, hindering the widespread adoption of reliable DL architectures in BCIs
outside research laboratories. To improve adoption, we need to improve user
comfort using, for instance, reliable algorithms that operate with few
electrodes. Approach: Our research aims to develop a DL algorithm that delivers
effective results with a limited number of electrodes. Taking advantage of the
Augmented Covariance Method with SPDNet, we propose the SPDNet$_{\psi}$
architecture and analyze its performance and computational impact, as well as
the interpretability of the results. The evaluation is conducted on 5-fold
cross-validation, using only three electrodes positioned above the Motor
Cortex. The methodology was tested on nearly 100 subjects from several
open-source datasets using the Mother Of All BCI Benchmark (MOABB) framework.
Main results: The results of our SPDNet$_{\psi}$ demonstrate that the augmented
approach combined with the SPDNet significantly outperforms all the current
state-of-the-art DL architecture in MI decoding. Significance: This new
architecture is explainable, with a low number of trainable parameters and a
reduced carbon footprint.
