Geometric neural network based on phase space for BCI-EEG decoding.

\textbf{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.

Classification of BCI-EEG Based on the Augmented Covariance Matrix.

Objective: Electroencephalography signals are recorded as multidimensional datasets. We propose a new framework based on the augmented covariance that stems from an autoregressive model to improve motor imagery classification.

Methods: From the autoregressive model can be derived the Yule-Walker equations, which show the emergence of a symmetric positive definite matrix: the augmented covariance matrix.

Pseudo-online framework for BCI evaluation: a MOABB perspective using various MI and SSVEP datasets.

. BCI (Brain-Computer Interfaces) operate in three modes:,, and. Inmode, real-time EEG data is constantly analyzed.