Study on an online collaborative BCI to accelerate response to visual targets

Peng Yuan, Yijun Wang, Wei Wu, Honglai Xu, Xiaorong Gao, Shangkai Gao
2012 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
Using brain-computer interfaces (BCIs) to improve human performance has become a state-of-the-art research topic. The concept of collaborative BCIs, which aimed to use multi-brain computing to enhance human performance, was proposed recently. To further study the feasibility of collaborative BCIs, here we propose to develop an online collaborative BCI to accelerate human response to visual target stimuli by detecting multi-subjects' visual evoked potentials (VEPs). A spatial filtering algorithm
more » ... which maximized the signal-to-noise ratio was used to extract VEP components from multichannel EEG. A two-layer support vector machine was subsequently used for target detection. Results of an offline analysis indicated that the system could achieve high accuracies (above 90%) at the stage before the behavioral response time (RT) (332±98ms). In online experiments with three groups of participants (each with three subjects), the system achieved significantly enhanced accuracies (79%, 82%, and 95% for three groups, respectively) at 120 ms after the target onset, which on average was 11% higher than the average individual accuracy, and 6% higher than the best individual accuracy.
doi:10.1109/embc.2012.6346284 fatcat:dlinaxbu4bawbarpqupzno4udy