  Recognition of electroencephalographic (EEG) signals highly affect the
efficiency of non-invasive brain-computer interfaces (BCIs). While recent
advances of deep-learning (DL)-based EEG decoders offer improved performances,
the development of geometric learning (GL) has attracted much attention for
offering exceptional robustness in decoding noisy EEG data. However, there is a
lack of studies on the merged use of deep neural networks (DNNs) and geometric
learning for EEG decoding. We herein propose a manifold attention network
(mAtt), a novel geometric deep learning (GDL)-based model, featuring a manifold
attention mechanism that characterizes spatiotemporal representations of EEG
data fully on a Riemannian symmetric positive definite (SPD) manifold. The
evaluation of the proposed MAtt on both time-synchronous and -asyncronous EEG
datasets suggests its superiority over other leading DL methods for general EEG
decoding. Furthermore, analysis of model interpretation reveals the capability
of MAtt in capturing informative EEG features and handling the non-stationarity
of brain dynamics.

Machine Learning;Signal Processing;Neurons and Cognition