A collaborative brain-computer interface for accelerating human decision making

  • Authors:

  • Peng Yuan;Yijun Wang;Xiaorong Gao;Tzyy-Ping Jung;Shangkai Gao

  • Affiliations:

  • Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China;Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego, San Diego;Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China;Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego, San Diego;Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China

  • Venue:
  • UAHCI'13 Proceedings of the 7th international conference on Universal Access in Human-Computer Interaction: design methods, tools, and interaction techniques for eInclusion - Volume Part I
  • Year:
  • 2013

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Abstract

Recently, collective intelligence has been introduced to brain-computer interface (BCI) research, leading to the emergence of collaborative BCI. This study presents an online collaborative BCI for improving individuals' decision making in a visual Go/NoGo task. Six groups of six people participated in the experiment comprising both offline and online sessions. The offline results suggested that the collaborative BCI has the potential to improve individuals' decisions in various decision-making situations. The online tests showed that using Electroencephalogram (EEG) within the first 360 ms after the stimulus onset, which was 50 ms earlier than the mean behavioral response time (RT) (409±85 ms), the collaborative BCI reached a mean classification accuracy of 78.0±2.6% across all groups. It was 12.9% higher than the average individual accuracy (65.1±8.1%, p−4). This study suggested that a collaborative BCI could accelerate human decision making with reliable prediction accuracy in real time.