An Individual-Adapted SSVEP Decoding Algorithm Based on Hybrid Augmentation and Collaborative Discrimination

Steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs) are consttablerained by three core challenges: data scarcity, individual variability, and temporal instability. To mitigate these issues, this study proposes an individual-adapted SSVEP decoding model that integrates hybrid augmentation, collaborative discrimination, and a Double-Area network within an ensemble learning framework, thus covering data expansion, feature discrimination, and multi-band harmonic extraction. The model enlarges the training set through hybrid augmentation using Source Aliasing Matrix Estimation (SAME) and Phase-Locked Time-Shift (PLTS), enhances feature robustness and class separability via collaborative discrimination using ensemble task-related component analysis with Kendall’s coefficient (eTRCA-K) and task-discriminant component analysis (TDCA), and effectively captures multi-band harmonics through a Double-Area network based on multi-reference least-squares transformations. Experimental results on the Benchmark dataset demonstrate that the proposed framework achieves strong adaptation capability and improved generalization, consistently outperforming existing methods across all data lengths and achieving an average accuracy of 88%. These findings highlight the model’s potential to further enhance SSVEP decoding performance and support high-performance target detection in BCI applications.