APCformer: an aggregation-perception enhanced convolutional transformer network for MI-EEG decoding

Electroencephalogram (EEG) decoding is essential for Brain-computer interfaces (BCI) systems to predict brain activity. However, existing methods usually suffer from two core problems: (1) existing networks lack effective interaction mechanisms and insufficiently capture spatial-temporal dynamic features, leading to the loss of critical fine-grained information; (2) the modeling of long-range dependencies and local features is unbalanced, making it difficult to adapt to the temporal characteristics of EEG signals. To address these issues, this paper proposes an Aggregation-Perception Enhanced Convolutional Transformer (APCformer) network. The network adopts a branch-interactive structure as its main body and jointly extracts shallow features via multi-scale spatial-temporal convolution; an Adaptive Feature Recalibration (AFR) module is embedded to realize cross-scale feature interaction and enhancement of critical fine-grained features. The Position-aware Enhancement (PAE) module is utilized to integrate learnable positional encoding, improving the ability of deep networks to characterize the temporal positional relationships of EEG sequences and enhancing adaptability to temporal dynamic features. We further propose a Sparse Information Aggregation Transformer (SAT), which combines the attention mechanism with the maximum attention mechanism to achieve a balanced modeling of global long-term dependencies and local fine-grained features. Experimental results on the public BCI-IV 2a and BCI-IV 2b datasets show that APCformer achieves superior performance in EEG decoding tasks, with average decoding accuracies of 85.53% and 89.15%, respectively. These results highlight APCformer's strong capability in handling complex EEG features and dynamic patterns, effectively improving the efficiency and accuracy of EEG decoding.