A Common Spatial Pattern Driven Temporal-Spatial Convolutional Neural Network for EEG-based Auditory Attention Decoding

Auditory attention decoding (AAD) from electroencephalography (EEG) remains challenging because complex spatiotemporal neural patterns are difficult to model, and decoding performance often degrades under short decision windows. This study aimed to develop an efficient and interpretable model for improving short-window AAD performance. A common spatial pattern (CSP)-driven shallow temporal-spatial convolutional neural network (TSCNN) was proposed. CSP spatial filtering was first used to enhance spatially discriminative EEG features associated with attention direction. The filtered signals were then processed by a spatiotemporal convolutional architecture composed of temporal and spatial convolution layers to capture local neural dynamics and cross-channel spatial information. Average pooling and fully connected layers were used for classification. The model was evaluated on the KUL and DTU datasets, and sensitivity and ablation analyses were conducted to assess the contributions of key modules. The proposed model outperformed several state-of-the-art methods on both datasets, particularly under short decision-window conditions. Ablation results further showed that the CSP module, temporal convolution, and spatial convolution each made important contributions to spatial discrimination, short-term dynamic modeling, and multi-channel information integration. Spatial filter visualizations revealed prominent activations in auditory-related cortical regions. The CSP-driven shallow TSCNN provides an effective, interpretable, and low-latency solution for auditory attention decoding and offers methodological support for practical AAD applications.