Geometric Neural Network based on Phase Space for BCI decoding (2024)
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1 Abstract
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 Interfaces (BCIs). A typical issue is the inherent nature of Electroencephalography (EEG) signals, which are small in sample size, non-stationary, and contaminated with physiological artifacts. In this paper, we propose a novel geometric framework (Phase-SPDNet) based on recent advances in geometric Deep Learning (DL) to classify EEG signals in motor imagery (MI) tasks by exploiting the symmetric positive definite (SPD) matrix structure of the data.
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