This study addresses the significant challenge of developing efficient decoding algorithms for classifying steady-state visual evoked potentials (SSVEPs) in scenarios characterized by extreme scarcity of calibration data, where only one calibration is available for each stimulus target. Please kindly cite our paper if you use our code.
@article{deng2023cross, title={Cross-subject dual-domain fusion network with task-related and task-discriminant component analysis enhancing one-shot SSVEP classification}, author={Deng, Yang and Ji, Zhiwei and Wang, Yijun and Zhou, S Kevin}, journal={arXiv preprint arXiv:2311.07932}, year={2023} }