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电极数量和解码算法对基于脑电图的在线脑机接口行为表现的影响研究

A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance.

作者信息

Meng Jianjun, Edelman Bradley J, Olsoe Jaron, Jacobs Gabriel, Zhang Shuying, Beyko Angeliki, He Bin

机构信息

Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States.

Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States.

出版信息

Front Neurosci. 2018 Apr 6;12:227. doi: 10.3389/fnins.2018.00227. eCollection 2018.

Abstract

Motor imagery-based brain-computer interface (BCI) using electroencephalography (EEG) has demonstrated promising applications by directly decoding users' movement related mental intention. The selection of control signals, e.g., the channel configuration and decoding algorithm, plays a vital role in the online performance and progressing of BCI control. While several offline analyses report the effect of these factors on BCI accuracy for a single session-performance increases asymptotically by increasing the number of channels, saturates, and then decreases-no online study, to the best of our knowledge, has yet been performed to compare for a single session or across training. The purpose of the current study is to assess, in a group of forty-five subjects, the effect of channel number and decoding method on the progression of BCI performance across multiple training sessions and the corresponding neurophysiological changes. The 45 subjects were divided into three groups using Laplacian Filtering (LAP/S) with nine channels, Common Spatial Pattern (CSP/L) with 40 channels and CSP (CSP/S) with nine channels for online decoding. At the first training session, subjects using CSP/L displayed no significant difference compared to CSP/S but a higher average BCI performance over those using LAP/S. Despite the average performance when using the LAP/S method was initially lower, but LAP/S displayed improvement over first three sessions, whereas the other two groups did not. Additionally, analysis of the recorded EEG during BCI control indicates that the LAP/S produces control signals that are more strongly correlated with the target location and a higher R-square value was shown at the fifth session. In the present study, we found that subjects' average online BCI performance using a large EEG montage does not show significantly better performance after the first session than a smaller montage comprised of a common subset of these electrodes. The LAP/S method with a small EEG montage allowed the subjects to improve their skills across sessions, but no improvement was shown for the CSP method.

摘要

基于运动想象的脑机接口(BCI)利用脑电图(EEG),通过直接解码用户与运动相关的心理意图,已展现出颇具前景的应用。控制信号的选择,例如通道配置和解码算法,对BCI控制的在线性能及进展起着至关重要的作用。虽然多项离线分析报告了这些因素对单次会话BCI准确性的影响——随着通道数量增加,性能渐近提高,达到饱和后下降——但据我们所知,尚未有在线研究对单次会话或跨训练进行比较。本研究的目的是在45名受试者中评估通道数量和解码方法对多轮训练中BCI性能进展及相应神经生理变化的影响。45名受试者被分为三组,分别使用具有9个通道的拉普拉斯滤波(LAP/S)、具有40个通道的共同空间模式(CSP/L)和具有9个通道的CSP(CSP/S)进行在线解码。在第一次训练会话中,使用CSP/L的受试者与使用CSP/S的受试者相比无显著差异,但平均BCI性能高于使用LAP/S的受试者。尽管使用LAP/S方法时的平均性能最初较低,但LAP/S在前三个会话中表现出改善,而其他两组则没有。此外,对BCI控制期间记录的脑电图的分析表明,LAP/S产生的控制信号与目标位置的相关性更强,并且在第五次会话时显示出更高的决定系数值。在本研究中,我们发现,使用大型脑电图导联的受试者在第一次会话后的平均在线BCI性能并不比由这些电极的共同子集组成的较小导联显著更好。使用小型脑电图导联的LAP/S方法使受试者能够在多个会话中提高技能,但CSP方法未显示出改善。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca19/5897442/fa1f58737d45/fnins-12-00227-g0001.jpg

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