Facilitating Applications of SSVEP-Based BCIs by Within-Subject Information Transfer - Liu, Xiaobing; Liu, Bingchuan; Dong, Guoya; Gao, Xiaorong; Wang, Yijun (Frontiers in Neuroscience, 2022)
The steady-state visual evoked potential based on the brain-computer interface (SSVEP-BCI) can provide high-speed alternative and augmentative communication in real-world applications. For individuals using a long-term brain-computer interface (BCI), within-subject (i.e., cross-day and cross-electrode) transfer learning could improve the BCI performance and reduce the calibration burden. To validate the transfer learning-based within-subject, this study designs a 40-target SSVEP-BCI. Sixteen subjects are recruited, each of whom has performed experiments on three different days and has undergone the experiments of the SSVEP-BCIs based on the dry and wet electrodes. Several transfer directions, including the cross-day directions in parallel with the cross-electrode directions, are analyzed, and it is found that the transfer learning-based approach can maintain stable performance by zero training. Compared with the fully calibrated approaches, the transfer learning-based approach can achieve significantly better or comparable performance in different transfer directions. This result verifies that the transfer learning-based scheme is well suited for implementing a high-speed zero-training SSVEP-BCI, especially the dry electrode-based SSVEP-BCI system. A validation experiment of the cross-day wet-to-dry transfer, involving nine subjects, has shown that the average accuracy is 85.97 ± 5.60 % for the wet-to-dry transfer and 77.69 ± 6.42 % for the TRCA-based dry electrode. By leveraging the EEG data acquired on different days by different electrodes via transfer learning, this study lays the foundation for facilitating the long-term usage of the SSVEP-BCI and advancing the frontier of the dry electrode-based SSVEP-BCI in real-world applications.