Shallow Convolution and Parallel Coarse‐To‐Fine Attention for Brain Signal Classification

Electroencephalography (EEG) signals, due to their non‐invasiveness, high temporal resolution, and low cost, have demonstrated broad application prospects in fields such as Brain‐Computer Interface (BCI), motor rehabilitation, and emotion recognition. However, the inherently low signal‐to‐noise ratio, non‐stationarity, and high dimensionality of EEG signals pose substantial challenges for signal decoding. To address these issues and improve the accuracy and robustness of EEG classification, this study proposes a novel EEG classification architecture for motor imagery data—Parallel Hybrid CNN‐Transformer (PHCT). The PHCT model consists of a shallow feature extraction module and parallel fine‐grained and coarse‐grained feature extractors. The former efficiently captures temporal and spatial local features using separable convolutions, while the latter integrates multi‐head self‐attention mechanisms with convolutional structures to model global and local dependencies. Additionally, data augmentation and Gaussian noise injection are introduced to enhance the model's generalization ability. Empirical studies conducted on the BCI Competition IV‐2b dataset show that the proposed model outperforms existing methods across nine subjects, achieving at least a 4.2% improvement in average classification accuracy compared to the current best model, resulting in a significant model performance boost. The experimental results are visualized using the t‐SNE method. This study provides a new effective pathway for EEG decoding in complex environments.