Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, San Diego, California, United States of America.
PLoS One. 2011;6(5):e20422. doi: 10.1371/journal.pone.0020422. Epub 2011 May 31.
Electroencephalogram (EEG) based brain-computer interfaces (BCI) have been studied since the 1970s. Currently, the main focus of BCI research lies on the clinical use, which aims to provide a new communication channel to patients with motor disabilities to improve their quality of life. However, the BCI technology can also be used to improve human performance for normal healthy users. Although this application has been proposed for a long time, little progress has been made in real-world practices due to technical limits of EEG. To overcome the bottleneck of low single-user BCI performance, this study proposes a collaborative paradigm to improve overall BCI performance by integrating information from multiple users. To test the feasibility of a collaborative BCI, this study quantitatively compares the classification accuracies of collaborative and single-user BCI applied to the EEG data collected from 20 subjects in a movement-planning experiment. This study also explores three different methods for fusing and analyzing EEG data from multiple subjects: (1) Event-related potentials (ERP) averaging, (2) Feature concatenating, and (3) Voting. In a demonstration system using the Voting method, the classification accuracy of predicting movement directions (reaching left vs. reaching right) was enhanced substantially from 66% to 80%, 88%, 93%, and 95% as the numbers of subjects increased from 1 to 5, 10, 15, and 20, respectively. Furthermore, the decision of reaching direction could be made around 100-250 ms earlier than the subject's actual motor response by decoding the ERP activities arising mainly from the posterior parietal cortex (PPC), which are related to the processing of visuomotor transmission. Taken together, these results suggest that a collaborative BCI can effectively fuse brain activities of a group of people to improve the overall performance of natural human behavior.
基于脑电图（EEG）的脑机接口（BCI）自 20 世纪 70 年代以来就一直受到研究。目前，BCI 研究的主要焦点在于临床应用，旨在为运动障碍患者提供新的沟通渠道，以提高他们的生活质量。然而，BCI 技术也可用于改善正常健康用户的人类表现。尽管这种应用已经提出了很长时间，但由于 EEG 的技术限制，在实际应用中几乎没有取得进展。为了克服单用户 BCI 性能低的瓶颈，本研究提出了一种协作范式，通过整合来自多个用户的信息来提高整体 BCI 性能。为了测试协作 BCI 的可行性，本研究定量比较了协作和单用户 BCI 在运动规划实验中从 20 名受试者收集的 EEG 数据上的分类精度。本研究还探索了融合和分析来自多个受试者 EEG 数据的三种不同方法：（1）事件相关电位（ERP）平均法，（2）特征连接法，以及（3）投票法。在使用投票法的演示系统中，预测运动方向（向左伸手与向右伸手）的分类精度从 66%分别提高到 80%、88%、93%和 95%，随着受试者数量从 1 增加到 5、10、15 和 20。此外，通过解码主要源自后顶叶皮层（PPC）的 ERP 活动，可以在受试者实际运动反应之前大约 100-250ms 做出伸手方向的决策，这与视觉运动传递的处理有关。总之，这些结果表明，协作 BCI 可以有效地融合一群人的大脑活动，以提高自然人类行为的整体性能。
