Removal of Movement Artifact From High-Density EEG Recorded During Walking and Running
Removal of Movement Artifact From High-Density EEG Recorded During Walking and Running
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DOI：
10.1152/jn.00105.2010
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发表时间：
2010-06-01
影响因子：
2.5
通讯作者：
Ferris, Daniel P.
中科院分区：
文献类型：
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作者：
Gwin, Joseph T.;Gramann, Klaus;Ferris, Daniel P.
Gwin JT，Gramann K，Makeig S，Ferris DP.步行和跑步时高密度脑电运动伪影的去除J Neurophysiol 103：3526-3534，2010.首次发表于2010年4月21日; doi：10.1152/jn.00105.2010。虽然人类的认知通常发生在动态的运动动作中，但大多数关于人脑动力学的研究都是在静态的坐姿或俯卧状态下进行的。EEG信号在历史上被认为是太容易产生噪声而不能记录人类运动期间的大脑动力学。在这里，我们应用了基于通道的伪影模板回归过程和随后的空间滤波方法，以消除步态相关的运动伪影从步行和跑步过程中记录的EEG信号。我们首先使用步幅时间扭曲，以消除步态伪影从高密度EEG记录在视觉oddball的歧视任务，而步行和跑步。接下来，我们应用infomax独立分量分析（伊卡）来解析基于通道的噪声降低的EEG信号到最大独立分量（IC），然后执行基于组件的模板回归。应用基于通道或基于通道加基于组件的伪影抑制显著降低了行走和跑步期间1.5至8.5 Hz频率范围内的EEG频谱功率。在步行条件下，步态相关的伪影是非实质性的：事件相关电位（ERP），这是几乎相同的视觉古怪的歧视事件，而站着，应用降噪前后可见。在跑步状态下，步态相关伪影严重损害了EEG信号：只有在去除伪影后才能检测到IC过程的稳定平均ERP时程。这些研究结果表明，高密度EEG可用于研究全身运动过程中的脑动力学，并且可以使用模板回归程序来最小化来自节律性步态事件的机械伪影。
Gwin JT, Gramann K, Makeig S, Ferris DP. Removal of movement artifact from high-density EEG recorded during walking and running. J Neurophysiol 103: 3526-3534, 2010. First published April 21, 2010; doi:10.1152/jn.00105.2010. Although human cognition often occurs during dynamic motor actions, most studies of human brain dynamics examine subjects in static seated or prone conditions. EEG signals have historically been considered to be too noise prone to allow recording of brain dynamics during human locomotion. Here we applied a channel-based artifact template regression procedure and a subsequent spatial filtering approach to remove gait-related movement artifact from EEG signals recorded during walking and running. We first used stride time warping to remove gait artifact from high-density EEG recorded during a visual oddball discrimination task performed while walking and running. Next, we applied infomax independent component analysis (ICA) to parse the channel-based noise reduced EEG signals into maximally independent components (ICs) and then performed component-based template regression. Applying channel-based or channel-based plus component-based artifact rejection significantly reduced EEG spectral power in the 1.5- to 8.5-Hz frequency range during walking and running. In walking conditions, gait-related artifact was insubstantial: event-related potentials (ERPs), which were nearly identical to visual oddball discrimination events while standing, were visible before and after applying noise reduction. In the running condition, gait-related artifact severely compromised the EEG signals: stable average ERP time-courses of IC processes were only detectable after artifact removal. These findings show that high-density EEG can be used to study brain dynamics during whole body movements and that mechanical artifact from rhythmic gait events may be minimized using a template regression procedure.