MSTA-CNN an ultra-lightweight multi-scale temporal-spatial attention convolutional neural network for high-performance P300 spellers

Abstract Efficient P300 detection in brain-computer interfaces (BCIs) remains a challenge due to low signal-to-noise ratio (SNR) and the high computational cost of existing deep learning models, which limits their deployment in real-time applications. To address these issues, we propose MSTA-CNN, an ultra-lightweight multi-scale temporal-spatial attention convolutional neural network with only 3,153 trainable parameters. By synergizing depthwise separable convolutions for hierarchical feature extraction with a dual-branch attention mechanism designed to dynamically re-weight informative channels and temporal segments MSTA-CNN achieves robust end-to-end learning from minimally preprocessed EEG data. Extensive evaluations on the BCI Competition II Dataset IIb and III Dataset II demonstrate state-of-the-art character recognition accuracy, reaching 98% at 15 sequences and an average of 97.5% across subjects. Notably, the model yields a practical information transfer rate (ITR) of 13.72 bits/min, significantly outperforming baseline methods in critical low-repetition scenarios while being 10-50× lighter. The learned representations align closely with P300 neurophysiology, as evidenced by spatial importance maps and t-SNE visualizations. MSTA-CNN establishes a new benchmark for efficient P300 spellers, paving the way for portable, high-speed BCI systems for individuals with severe motor disabilities.