LARSNet: A Lightweight Asymmetric Residual Siamese Network for SSVEP-Based Brain–Computer Interfaces

Steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs) offer a high information transfer rate (ITR), but are often constrained by the computational burden and limited performance of existing decoding models. To address these challenges, we propose LARSNet, a lightweight asymmetric residual Siamese network tailored for efficient and accurate multiclass SSVEP classification. LARSNet employs an asymmetric architecture comprising a residual-enhanced signal branch for robust feature extraction from low signal-to-noise ratio (SNR) data and a reference branch for high-fidelity template construction. A correlation analysis layer with a parameter-efficient approximate weight mechanism further refines the discriminative capability by modeling joint stimulus interference. Comprehensive evaluations on 40-class and 12-class datasets demonstrated that LARSNet significantly outperformed traditional and deep learning methods, particularly for short data segments, while reducing model complexity to 491 parameters and 1.00 mega floating-point operations (MFLOPs). In particular, with 0.4-s data, the 40-class dataset achieved an ITR of$254.15~\pm ~69.51$bits/min, while the 12-class dataset reached$125.78~\pm ~34.26$bits/min. Feature visualization further revealed a progressive SNR enhancement of SSVEP characteristic frequencies across layers. Collectively, these results highlight the potential of LARSNet for SSVEP-BCI applications, offering a robust balance of high performance and low complexity.