Yilmaz Gizem, Ungan Pekcan, Türker Kemal S
Koc University School of Medicine, Rumelifeneri Yolu, Sariyer, 34450, Istanbul, Turkey.
Exp Brain Res. 2018 Apr;236(4):1007-1017. doi: 10.1007/s00221-018-5194-6. Epub 2018 Feb 5.
Electrodes for recording electroencephalogram (EEG) are placed on or around cranial muscles; hence, their electrical activity may contaminate the EEG signal even at rest conditions. Due to its role in maintaining mandibular posture, tonic activity of temporalis muscle interferes with the EEG signal particularly at fronto-temporal locations at single motor unit (SMU) level. By obtaining surface representation of a motor unit, we can evaluate its interference in EEG and if we could sum surface representations of several tonically active motor units, we could estimate the overall myogenic contamination in EEG. Therefore, in this study, we followed re-composition (RC) approach and generated EEG-like artefact model using surface representations of single motor units (RC). Furthermore, we compared signal characteristics of RC signals with simultaneously recorded EEG signal at different locations in terms of power spectral density and coherence. First, we found that RC signal represented the power spectral distribution of an EMG signal. Second, RC signal reflected the discharge rate of a SMU giving the greatest surface representation amplitude and strongest interference appeared as distinguishable frequency peak on RC power spectra. Moreover, for strong interferences, RC also contaminated the EEG at F7 and other EEG electrodes. These findings are important to illustrate the susceptibility of EEG signal to myogenic artefacts even at rest and the research using EEG coherence comparisons should consider muscle activity while drawing conclusions about neuronal interactions and oscillations.
用于记录脑电图(EEG)的电极放置在颅肌上或其周围;因此,即使在静息状态下,它们的电活动也可能会干扰EEG信号。由于颞肌在维持下颌姿势中所起的作用,其紧张性活动会干扰EEG信号,尤其是在单个运动单位(SMU)水平上的额颞部位置。通过获取运动单位的表面表征,我们可以评估其对EEG的干扰,如果我们能够将几个紧张性活动的运动单位的表面表征相加,就可以估计EEG中总的肌源性干扰。因此,在本研究中,我们采用重新合成(RC)方法,利用单个运动单位的表面表征生成类似EEG的伪迹模型(RC)。此外,我们在功率谱密度和相干性方面,比较了RC信号与在不同位置同时记录的EEG信号的信号特征。首先,我们发现RC信号代表了肌电信号的功率谱分布。其次,RC信号反映了具有最大表面表征幅度的SMU的放电率,并且最强的干扰表现为RC功率谱上可区分的频率峰值。此外,对于强烈干扰,RC也会在F7和其他EEG电极处干扰EEG信号。这些发现对于说明即使在静息状态下EEG信号对肌源性伪迹的敏感性很重要,并且在利用EEG相干性比较得出关于神经元相互作用和振荡的结论时,使用EEG的研究应考虑肌肉活动。