Enhancing One-Shot Ssvep Classification by Combining Cross-Subject Dual-Domain Fusion Network with Task-Related and Task-Discriminant Component Analysis

This study addresses the significant challenge of developing efficient decoding algorithms for classifying steadystate visual evoked potentials (SSVEPs) in scenarios characterized by extreme scarcity of calibration data, where only one calibration trial is available for each stimulus target. To tackle this problem, we introduce a novel cross-subject dual-domain fusion network (CSDuDoFN) that incorporates task-related and task-discriminant component analysis (TRCA and TDCA) for one-shot SSVEP classification. The CSDuDoFN framework is designed to comprehensively transfer information from source subjects, while TRCA and TDCA are employed to exploit the single available calibration trial of the target subject. Specifically, we develop multi-reference least-squares transformation (MLST) to map data from both source subjects and the target subject into the domain of sine-cosine templates, thereby mitigating interindividual variability and benefiting transfer learning. Subsequently, the transformed and original data are used separately to train a convolutional neural network (CNN) model, with an adequate fusion of their feature maps occurring at different network layers. To further capitalize on the calibration trial of the target subject, source aliasing matrix estimation (SAME)-based data augmentation is incorporated into the training process of the ensemble TRCA (eTRCA) and TDCA models. Ultimately, the outputs of CSDuDoFN, eTRCA, and TDCA are combined for the SSVEP classification. The effectiveness of our proposed approach is comprehensively evaluated on three publicly available SSVEP datasets, achieving the best performance on two datasets and competitive performance on the third. Further, it is worth noting that our method follows a different technical route from the current state-of-the-art (SOTA) method, and the two are complementary. The performance is significantly improved when our method is combined with the SOTA method. This study underscores the potential to integrate the brain-computer interface (BCI) into daily life. The corresponding source code is accessible at https://github.com/Sungden/One-shot-SSVEP-classification.