Geometric Neural Network based on Phase Space for BCI decoding

Igor Carrara (1) , Bruno Aristimunha (2, 3, 4) , Marie-Constance Corsi (5) , Raphael Y de Camargo (3) , Sylvain Chevallier (4, 2) , Théodore Papadopoulo (1)

1 CRONOS - Modélisation des résaux dynamiques cérébraux
2 Université Paris-Saclay
3 UFABC - Universidade Federal do ABC = Federal University of ABC = Université Fédérale de l'ABC [Brazil]
4 TAU - TAckling the Underspecified
5 NERV - Equipe Neuroingénierie des systèmes pour modéliser et interfacer les réseaux cérébraux = Systems neuroengineering to model and interface brain networks [ICM Paris]

Objective: 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 \method{} 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 method 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.

Keywords: Artificial Intelligence AI; Brain-Computer Interfaces; Electroencephalography; Functional connectivity; SPD manifold; Riemannian optimization; Neural Network; Motor Imagery; Deep leaning

Domains: Artificial Intelligence [cs.AI]; Computer Science [cs]; Human-Computer Interaction [cs.HC]

Igor Carrara, Bruno Aristimunha, Marie-Constance Corsi, Raphael Y de Camargo, Sylvain Chevallier, et al.. Geometric Neural Network based on Phase Space for BCI decoding. 2024. ⟨hal-04500580⟩