Independent Component Analysis of Gait-Related Movement Artifact Recorded using EEG Electrodes during Treadmill Walking.
Independent Component Analysis of Gait-Related Movement Artifact Recorded using EEG Electrodes during Treadmill Walking.
复制标题
DOI：
10.3389/fnhum.2015.00639
复制
发表时间：
2015
影响因子：
2.9
通讯作者：
Ferris DP
中科院分区：
文献类型：
--
作者：
Snyder KL;Kline JE;Huang HJ;Ferris DP
由于其便携性和良好的时间分辨率，最近在使用脑电图（EEG）作为移动脑成像工具方面出现了激增。当脑电图与独立分量分析（ICA）和源定位技术相结合时，可以将皮层电活动建模为由位于空间不同的皮层区域的时间独立信号引起的。然而，对于移动任务，尚不清楚运动工件如何影响ICA和源定位。我们设计了一种新的方法来收集纯运动伪影数据（没有任何电生理信号）与256通道的脑电图系统。我们首先使用硅胶泳帽阻止真正的皮层电活动。在硅胶层上，我们放置了一个具有与真实人类头皮相似电学特性的模拟头皮。我们收集了10名健康的年轻受试者在跑步机上以0.4-1.6米/秒的速度行走时的脑电图运动伪信号。我们对EEG运动伪信号数据进行了ICA和偶极子拟合，以量化这些方法将伪信号识别为非神经信号的准确性。ICA和偶极子拟合准确定位了99%的非神经位置或缺乏偶极子特征的独立分量。其余1%的源位于脑容量内，残差较小，但具有典型的非神经源的地形图、功率谱、时间过程和与事件相关的谱扰动。在解释包含半周期伪影（包括由人类行走产生的伪影）的ICA数据时应谨慎。移动脑电信号中运动伪影的识别和分离需要替代方法，特别是能够实时执行的方法。将真实的大脑信号从运动伪影中分离出来，可以为脑电图脑机接口扫清道路，从而在行走等移动活动中提供帮助。
There has been a recent surge in the use of electroencephalography (EEG) as a tool for mobile brain imaging due to its portability and fine time resolution. When EEG is combined with independent component analysis (ICA) and source localization techniques, it can model electrocortical activity as arising from temporally independent signals located in spatially distinct cortical areas. However, for mobile tasks, it is not clear how movement artifacts influence ICA and source localization. We devised a novel method to collect pure movement artifact data (devoid of any electrophysiological signals) with a 256-channel EEG system. We first blocked true electrocortical activity using a silicone swim cap. Over the silicone layer, we placed a simulated scalp with electrical properties similar to real human scalp. We collected EEG movement artifact signals from ten healthy, young subjects wearing this setup as they walked on a treadmill at speeds from 0.4–1.6 m/s. We performed ICA and dipole fitting on the EEG movement artifact data to quantify how accurately these methods would identify the artifact signals as non-neural. ICA and dipole fitting accurately localized 99% of the independent components in non-neural locations or lacked dipolar characteristics. The remaining 1% of sources had locations within the brain volume and low residual variances, but had topographical maps, power spectra, time courses, and event related spectral perturbations typical of non-neural sources. Caution should be exercised when interpreting ICA for data that includes semi-periodic artifacts including artifact arising from human walking. Alternative methods are needed for the identification and separation of movement artifact in mobile EEG signals, especially methods that can be performed in real time. Separating true brain signals from motion artifact could clear the way for EEG brain computer interfaces for assistance during mobile activities, such as walking.