Implementing Over 100 Command Codes for a High-Speed Hybrid Brain-Computer Interface Using Concurrent P300 and SSVEP Features

Abstract

Objective Recently, electroencephalography (EEG)- based brain-computer interfaces (BCIs) have made tremendous progress in increasing communication speed. However, current BCI systems could only implement a small number of command codes, which hampers their applicability. Methods This study developed a high-speed hybrid BCI system containing as many as 108 instructions, which were encoded by concurrent P300 and steady-state visual evoked potential (SSVEP) features and decoded by an ensemble task-related component analysis method. Notably, besides the frequency-phase-modulated SSVEP and time-modulated P300 features as contained in the traditional hybrid P300 and SSVEP features, this study found two new distinct EEG features for the concurrent P300 and SSVEP features, i.e., time-modulated SSVEP and frequency-phase- modulated P300. Ten subjects spelled in both offline and online cued-guided spelling experiments. Other ten subjects took part in online copy-spelling experiments. Results Offline analyses demonstrate that the concurrent P300 and SSVEP features can provide adequate classification information to correctly select the target from 108 characters in 1.7 seconds. Online cued-guided spelling and copy-spelling tests further show that the proposed BCI system can reach an average information transfer rate (ITR) of 172.46 ± 32.91 bits/min and 164.69 ± 33.32 bits/min respectively, with a peak value of 238.41 bits/min (The demo video of online copy-spelling can be found at https://www.youtube.com/watch?v=EW2Q08oHSBo). Conclusion We expand a BCI instruction set to over 100 command codes with high-speed in an efficient manner, which significantly improves the degree of freedom of BCIs. Significance This study hold promise for broadening the applications of BCI systems.

Abstract of "Implementing Over 100 Command Codes for a High-Speed Hybrid Brain-Computer Interface Using Concurrent P300 and SSVEP Features" by Xu M, Han J, Wang Y et al., https://doi.org/10.1109/tbme.2020.2975614, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.