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

机译:电极编号和解码算法对基于在线EEG的BCI行为表现的影响研究

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摘要

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.
机译:使用脑电图(EEG)的基于运动图像的脑机接口(BCI)通过直接解码用户与运动有关的心理意图,显示出了广阔的应用前景。控制信号的选择,例如通道配置和解码算法,在BCI控制的在线性能和进步中起着至关重要的作用。尽管有几项离线分析报告了这些因素对单个会话BCI准确性的影响-通过增加通道数量,饱和然后减少而逐渐提高性能,但据我们所知,尚未进行在线研究来比较单个课程还是整个培训。本研究的目的是评估一组四十五名受试者的通道数和解码方法对跨多次训练的BCI表现进展以及相应的神经生理学变化的影响。使用45个通道的Laplacian滤波(LAP / S),40个通道的公共空间模式(CSP / L)和9个通道的CSP(CSP / S)将45位受试者分为三组,用于在线解码。在第一次培训中,使用CSP / L的受试者与CSP / S相比没有显着差异,但是与使用LAP / S的受试者相比,平均BCI表现更高。尽管最初使用LAP / S方法时的平均性能较低,但是LAP / S在前三个疗程中表现出改善,而其他两组则没有。另外,在BCI控制期间对记录的EEG的分析表明,LAP / S产生的控制信号与目标位置的相关性更高,并且在第五次会话中显示出更高的R平方值。在本研究中,我们发现使用大型脑电图蒙太奇后,受试者的平均在线BCI表现并未比包含这些电极的公共子集的较小蒙太奇表现出明显更好的表现。 LAP / S方法的脑电图蒙太奇较小,可以使受试者在整个疗程中提高自己的技能,但CSP方法未见改善。

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