A Collaborative Brain-Computer Interface
Electroencephalogram (EEG) based brain-computer interfaces (BCI) have been studied for several decades since the 1970s. Current BCI research mainly aims to provide a new communication channel to patients with motor disabilities to improve their quality of life. The BCI technology can also benefit normal healthy users; however, little progress has been made in real-world practices due to low BCI performance caused by technical limits of EEG. To overcome this bottleneck, this study uses a collaborative BCI to improve overall performance through integrating information from multiple users. A dataset involving 15 subjects participating in a Go/NoGo decision-making experiment was used to evaluate the collaborative method. Using collaborative computing techniques, the classification accuracy for predicting a Go/NoGo decision was enhanced substantially from 75.8％ to 91.4％, 97.6％, and 99.1％ as the number of subjects increased from 1 to 5, 10, and 15, respectively. These results suggest that a collaborative BCI can effectively fuse brain activities of a group of people to improve human behavior.
Brain-computer interface (BCI) collaborative computing Electroencephalogram (EEG) human performance Go/NoGo decision making
Yijun Wang Yu-Te Wang Tzyy-Ping Jung Xiaorong Gao Shangkai Gao
Swartz Center for Computational Neuroscience University of California San Diego San Diego, USA Dept. Biomedical Engineering, School of Medicine Tsinghua University Beijing, China
国际会议
上海
英文
578-581
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）