Geometric Neural Network based on Phase Space for BCI-EEG decoding

TL;DR

This paper introduces Phase-SPDNet, a geometric neural network architecture based on phase space and SPDNet, which effectively decodes motor imagery from EEG signals using few electrodes, outperforming existing deep learning methods.

Contribution

The paper presents a novel Phase-SPDNet architecture that leverages augmented covariance and SPDNet for improved EEG decoding with limited electrodes, enhancing interpretability and performance.

Findings

Outperforms state-of-the-art DL architectures in MI decoding.

Effective with only three electrodes above the Motor Cortex.

Low number of trainable parameters and explainability.

Abstract

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. This is particularly true for 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 and the framework of SPDNet, we propose the Phase-SPDNet architecture and analyze its performance and 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 Phase-SPDNet 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 and with a low number of trainable parameters.