AMAtt: Manifold Attention Network with Adaptive Log-Euclidean Metrics for Brain Signals - Syed Shaihan (European Journal of Clinical and Biomedical Sciences, 2026)
In this paper, we discuss the recognition of electroencephalographic (EEG) signals, which is crucial in order to improve the performance of non-invasive brain-computer interfaces (BCIs). Although deep learning (DL) has achieved considerable advancements in the decoding of EEG signals, it frequently encounters difficulties related to noisy data and non-stationarity challenges. We discuss geometric learning which offers a more robust way to handle EEG signals by leveraging the mathematical structure of the data. We extended the existing Manifold Attention Network (MAtt), a novel deep learning model that applies a manifold attention mechanism to better capture the spatiotemporal patterns of EEG signals. Instead of using traditional Euclidean space, we mapped the data onto a Riemannian symmetric positive definite (SPD) manifold, which allows for more effective feature extraction. One major issue with existing SPD-based deep learning approaches is that they rely on fixed Riemannian metrics, which can be suboptimal. To solve this, we integrate Adaptive Log-Euclidean Metrics (ALEMs)---a learnable metric framework into the MAtt network that adapts to the specific structure of EEG data, improving model performance with minimal extra computation. AMAtt achieves 63.19% accuracy on BCIC-IV-2a and 46.00% on MAMEM-SSVEP-II, outperforming the MAtt baseline by 5.55% and 4.60% respectively. This adaptive geometric approach opens new possibilities for robust, subject-independent EEG decoding, paving the way towards practical BCI systems.