Poster Abstract: TCT: Zero-training two staged Contrastive Transformer network for SSVEP classification

Steady-State Evoked Potential (SSVEP) is a brain response to specific frequency visual stimuli, used in brain-computer interfaces due to its robust and easily detectable signals. Researchers have long applied methods like Canonical Correlation Analysis and deep learning for SSVEP signal decomposition and classification. However, those methods struggle to classify SSVEP signals without new subject’s data, and calibration is time-consuming. In this paper, we propose a two-stage, two-Transformer streams network to address the challenge of classifying SSVEP signals from new subjects. We utilize hierarchical contrastive learning to project features into a more discriminable feature space before classification. The comparative experiment demonstrates that our approach exhibits superior performance relative to alternative methods in processing SSVEP signals from new subjects.