DASTCN: Enhancing Cross-Subject P300 Detection via Adversarial Spatio-Temporal Learning and Adaptive Source Selection

Brain-Computer Interface BCI systems aim to decode neural activity and translate it into actionable commands for external devices. Electroencephalogram EEG is a widely used, non-invasive method for analyzing brain activity. However, the significant inter-subject variability in EEG signals poses a major challenge for the generalization of EEG-based models. While Domain-Adversarial Neural Networks DANN have demonstrated promising results in transfer learning tasks, their application to EEG-based cross-subject P300 detection remains relatively unexplored. In this study, we introduce the Domain-Adversarial Spatio-Temporal Convolution Network DASTCN , which combines a Generative Adversarial Network GAN with a lightweight spatio-temporal convolutional architecture to address the issue of inter-subject variability. Extensive empirical evaluations show that DASTCN outperforms conventional models, achieving an accuracy of 84.9 in cross-subject P300 detection. These findings underscore the potential of DASTCN as a transformative tool for advancing practical BCI systems and offer significant implications for future research and applications in this field.