Online control of a brain-computer interface using phase synchronization
Online control of a brain-computer interface using phase synchronization
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DOI：
10.1109/tbme.2006.881775
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发表时间：
2006-12-01
影响因子：
4.6
通讯作者：
Pfurtscheller, Gert
中科院分区：
文献类型：
--
作者：
Brunner, Clemens;Scherer, Reinhold;Pfurtscheller, Gert
目前，几乎所有脑机接口（BCIs）都忽略了从不同记录部位（即电极）检测到的脑电图信号相位之间的关系。绝大多数 BCI 系统依赖于从频带功率或单变量自适应自回归 (AAR) 参数导出的特征向量。然而，大量证据表明，通过量化单个电极信号之间的关系可以获得额外的信息，这可能为未来的脑机接口系统提供创新功能。本文研究了一种通过计算所谓的锁相值（PLV）来提取两个脑电图（EEG）信号之间的相位同步程度的方法。在我们的离线研究中，获取了几个基于 PLV 的特征，并通过特征选择算法为每个受试者单独选择了最佳特征集。与三名受过训练的受试者进行的在线课程显示，所有受试者在整个课程中都能够控制三种心理状态（分别是左手、右手和脚的运动想象），单次试验准确率在 60% 到 66.7% 之间（偶然预计为 33%）。
Currently, almost all brain-computer interfaces (BCIs) ignore the relationship between phases of electroencephalographic signals detected from different recording sites (i.e., electrodes). The vast majority of BCI systems rely on feature vectors derived from e.g., bandpower or univariate adaptive autoregressive (AAR) parameters. However, ample evidence suggests that additional information is obtained by quantifying the relationship between signals of single electrodes, which might provide innovative features for future BCI systems. This paper investigates one method to extract the degree of phase synchronization between two electroencephalogram (EEG) signals by calculating the so-called phase locking value (PLV). In our offline study, several PLV-based features were acquired and the optimal feature set was selected for each subject individually by a feature selection algorithm. The online sessions with three trained subjects revealed that all subjects were able to control three mental states (motor imagery of left hand, right hand, and foot, respectively) with single-trial accuracies between 60% and 66.7% (33% would be expected by chance) throughout the whole session.