Variational spatiotemporal enhanced transformer encoder-decoder for robust EEG signal recognition

Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) decode neural intent but remain vulnerable to non-stationarity, low signal-to-noise ratios, complex spatiotemporal dynamics, and inter-trial variability. Existing deterministic deep models often fail to capture global temporal dependencies while preserving local spatial topology, limiting robustness and generalization. We propose the Variational Spatiotemporal Enhanced Transformer (VSTE-Transformer), an encoder–decoder framework for robust EEG recognition. It embeds variational inference into a Transformer, representing latent features as distributions to quantify uncertainty and reduce sensitivity to noise. A Spatiotemporal Enhancement Module combines localized graph attention, which models dynamic electrode correlations, with multi-scale dilated convolutions that capture rhythmic temporal contexts. A dual-task objective jointly optimizes classification and signal reconstruction, encouraging the latent manifold to preserve neurophysiologically meaningful structure. Experiments on BCI Competition IV-2a and OpenBMI under a unified within-subject, cross-session protocol show that VSTE-Transformer significantly outperforms state-of-the-art methods and remains more robust under severe noise corruption.