MB-RSVP: multi-brain can improve accuracy of single-trial EEG-based object detection

The EEG-based rapid serial visual presentation (RSVP) brain-computer interface (BCI) paradigm has been widely used to achieve quickly target detection due to its potential in detecting an interest target from a rapidly refreshing letter or image stream. In these applications, single-trial RSVP detection accuracy is very important but it is often compromised by the low signal-to-noise ratio and and non-linearity of the EEG signal. In this study, we established a multi-brain collaborative BCI paradigm and designed a multi-brain cooperative RSVP detection (MB-RSVP) algorithm based on multi-variable linear regression models. Using this method, the single-trial classification accuracy of the three RSVP paradigms was estimated. Online experiment results indicated that the proposed multi-brain collaborative BCI-based RSVP framework effectively improves classification accuracy compared to single-subject RSVP while retaining real-time performance. Furthermore, offline experiment results showed that the proposed method improved the classification accuracy by 20.48% compared to single-subject RSVP experiments. The proposed multi-brain collaborative BCI-based RSVP framework provides a significant impact for driving EEG-based RSVP towards real-world applications.