Enhancing Detection of Control State for High-Speed Asynchronous SSVEP-BCIs Using Frequency-Specific Framework.

This study proposed a novel frequency-specific (FS) algorithm framework for enhancing control state detection using short data length toward high-performance asynchronous steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCI). The FS framework sequentially incorporated task-related component analysis (TRCA)-based SSVEP identification and a classifier bank containing multiple FS control state detection classifiers. For an input EEG epoch, the FS framework first identified its potential SSVEP frequency using the TRCA-based method and then recognized its control state using one of the classifiers trained on the features specifically related to the identified frequency. A frequency-unified (FU) framework that conducted control state detection using a unified classifier trained on features related to all candidate frequencies was proposed to compare with the FS framework. Offline evaluation using data lengths within 1 s found that the FS framework achieved excellent performance and significantly outperformed the FU framework. 14-target FS and FU asynchronous systems were separately constructed by incorporating a simple dynamic stopping strategy and validated using a cue-guided selection task in an online experiment. Using averaged data length of 591.63±5.65 ms, the online FS system significantly outperformed the FU system and achieved an information transfer rate, true positive rate, false positive rate, and balanced accuracy of 124.95±12.35 bits/min, 93.16±4.4%, 5.21±5.85%, and 92.89±4.02%, respectively. The FS system was also of higher reliability by accepting more correctly identified SSVEP trials and rejecting more wrongly identified ones. These results suggest that the FS framework has great potential to enhance the control state detection for high-speed asynchronous SSVEP-BCIs.