CNN-Enhanced Transformer with Transfer Learning for Cross-Session Monkey Motor Neural Decoding

Cross-session variability in neural signal statistics often leads to distribution shifts that substantially degrade the generalization performance of motor decoding models. To address this challenge, this study proposes a CNN-enhanced Transformer with distribution alignment (CNN-Trans-DA), which integrates local temporal feature extraction with global sequence modeling and a lightweight domain adaptation strategy. A one-dimensional convolutional module is introduced prior to the Transformer encoder to capture local firing patterns, while multi-head attention is used to learn long-range temporal dependencies. For cross-session adaptation, we design a joint distribution alignment method that simultaneously minimizes marginal and conditional distribution discrepancies through maximum mean discrepancy (MMD) and correlation alignment (CORAL). Using four-direction monkey fingertip movement data, the proposed CNN-Trans-DA model achieves an accuracy of 84.64 percent under single-session decoding, outperforming the standard Transformer (81.22%), CNN (80.78%), and BiLSTM (79.29%). In cross-session transfer experiments, the model maintains an 82.42 percent decoding accuracy, showing clear improvements over models without distribution alignment. These results demonstrate that the proposed approach can effectively handle session-induced distribution drift and achieve robust motor intention decoding, providing a scalable and generalizable solution for cross-session brain–machine interface applications.