SRRNet: Unseen SSVEP Response Regression From Stimulus for Cross-Stimulus Transfer in SSVEP-BCIs.

The prolonged calibration time required by steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) poses a significant challenge to real-life applications. Cross-stimulus transfer emerges as a promising solution, wherein a model trained on a subset of classes (seen classes) can predict both seen and unseen classes. Existing approaches extracted common components from SSVEP templates of seen classes to construct templates for unseen classes; however, they are limited by the class-specific activities and noise contained in these components, leading to imprecise templates that degrade classification performance. To address this issue, this study proposed an SSVEP Response Regression Network (SRRNet), which learned the regression mapping between sine-cosine reference signals and SSVEP templates using seen class data. This network reconstructed SSVEP templates for unseen classes utilizing their corresponding sine-cosine signals. Additionally, an SSVEP template regressing and spatial filtering (SRSF) framework was introduced, where both test data and SSVEP templates were projected by task-related component analysis (TRCA) spatial filters, and correlations were computed for target prediction. Comparative evaluations on two public datasets revealed that our method significantly outperformed state-of-the-art methods, elevating the information transfer rate (ITR) from 173.33 bits/min to 203.79 bits/min. By effectively modeling the regression from sine-cosine reference signals to SSVEP templates, SRRNet can construct SSVEP templates for unseen classes without training samples from those classes. By integrating regressed SSVEP templates with spatial filtering-based methods, our method enhances cross-stimulus transfer performance in SSVEP-BCIs, thus advancing their practical applicability. The code is available at https://github.com/MaiXiming/SRRNet.