A high-speed hybrid brain-computer interface with more than 200 targets

Objective. Brain-computer interfaces (BCIs) have recently made significant strides in expanding their instruction set, which has attracted wide attention from researchers. The number of targets and commands is a key indicator of how well BCIs can decode the brain's intentions. No studies have reported a BCI system with over 200 targets. Approach. This study developed the first high-speed BCI system with up to 216 targets that were encoded by a combination of electroencephalography features, including P300, motion visual evoked potential (mVEP), and steady-state visual evoked potential (SSVEP). Specifically, the hybrid BCI paradigm used the time-frequency division multiple access strategy to elaborately tag targets with P300 and mVEP of different time windows, along with SSVEP of different frequencies. The hybrid features were then decoded by task-discriminant component analysis and linear discriminant analysis. Ten subjects participated in the offline and online cued-guided spelling experiments. Other ten subjects took part in online free-spelling experiments. Main results. The offline results showed that the mVEP and P300 components were prominent in the central, parietal, and occipital regions, while the most distinct SSVEP feature was in the occipital region. The online cued-guided spelling and free-spelling results showed that the proposed BCI system achieved an average accuracy of 85.37% +/- 7.49% and 86.00% +/- 5.98% for the 216-target classification, resulting in an average information transfer rate (ITR) of 302.83 +/- 39.20 bits min(-1) and 204.47 +/- 37.56 bits min(-1), respectively. Notably, the peak ITR could reach up to 367.83 bits min(-1). Significance. This study developed the first high-speed BCI system with more than 200 targets, which holds promise for extending BCI's application scenarios.

Automated summary

This study developed a high-speed BCI system with more than 200 targets, encoded by a combination of electroencephalography features. The system achieved high accuracy and information transfer rate in offline and online experiments, showing promise for extending BCI's application scenarios.