Background

Identifying which EEG signals distinguish left from right leg movements in imagined lower limb movement is crucial to building an effective and efficient brain-computer interface (BCI). Past findings on this issue have been mixed, partly due to the difficulty in collecting and isolating the relevant information. The purpose of this study was to contribute to this new and important literature.

Research Question

Can left versus right imagined stepping be differentiated using the alpha, beta, and gamma frequencies of EEG data at four electrodes (C1, C2, PO3, and PO4)?

Methods

An experiment was conducted with a sample of 16 healthy male participants. They imagined left and right lower limb movements across 60 trials at two time periods separated by one week. Participants were fitted with a 64-electrode headcap, lay supine on a specially designed device and then completed the imagined task while observing a customized computer-generated image of a human walking to signify the left and right steps, respectively.

Results

Findings showed that eight of the twelve frequency bands from 4 EEG electrodes were significant in differentiating imagined left from right lower limb movement. Using these data points, a neural network analysis resulted in an overall participant average test classification accuracy of left versus right movements at 63 %.

Significance

Our study provides support for using the alpha, beta and gamma frequency bands at the sensorimotor areas (C1 and C2 electrodes) and incorporating information from the parietal/occipital lobes (PO3 and PO4 electrodes) for focused, real-time EEG signal processing to assist in creating a BCI for those with lower limb compromised mobility.

中文翻译：

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脑电图区分左右下肢想象的运动

背景

识别出想象中的下肢运动中哪些EEG信号将左腿运动与右腿运动区分开，对于建立有效而高效的脑机接口（BCI）至关重要。过去在此问题上的发现参差不齐，部分是由于难以收集和隔离相关信息。这项研究的目的是为这一新的重要文献做出贡献。

研究问题

是否可以使用四个电极（C1，C2，PO3和PO4）上的EEG数据的alpha，beta和gamma频率来区分左右想象的步进？

方法

对16名健康男性参与者的样本进行了实验。他们想象了在60个试验中左右两个下肢的运动在两个时间间隔（一周）之间进行。参与者安装有64电极头罩，仰卧在专门设计的设备上，然后完成想象的任务，同时观察定制的计算机生成的人类步行图像，分别表示左脚步和右脚步。

结果

研究结果表明，来自4个EEG电极的十二个频段中的八个在区分想象的左下肢运动与右下肢运动方面具有显着意义。使用这些数据点，神经网络分析得出参与者总体左，右运动的平均测试分类准确度为63％。

意义

我们的研究为在感觉运动区域（C1和C2电极）使用alpha，beta和gamma频带以及合并来自顶叶/枕叶（PO3和PO4电极）的信息以进行集中的实时EEG信号处理提供了支持为下肢行动不便的人创建BCI。