脑电信号中肌电伪差的实时去除方法研究

为了在线去除脑电信号中的肌电伪差,使用典型相关分析方法,分析了大量被肌电干扰和未被干扰的脑电(EEG)信号,得出了一个合理的自相关阈值.在时域上,肌电伪差和一般的噪声信号比较类似,有比较小的自相关值,在去除肌电伪差时,凡自相关值低于此值的分解成分被识别为肌电伪差.该方法很好地利用了这个特点,将肌电伪差分量与潜在大脑信号分离,然后依据剩下的分解成分重建"干净"的EEG信号.实验结果表明,典型相关分析法在去除肌电伪差时优于独立成分分析法,结合提出的自相关阈值在有效去除肌电伪差的同时,又能较好地保持潜在的大脑信号不变.

Electromyography Artifact Removal from Electroencephalogram in Real-Time

To remove electromyography (EMG) artifacts from electroencephalogram (EEG) signals real-time, canonical correlation analysis (CCA) is adopted. By analyzing a number of clean' and contaminated electroencephalogram (EEG) signals using CCA, a reasonable correlation threshold is obtained. The EMG artifacts are similar to the common noise in time domain. Hence, the EMG artifacts components obtained by CCA have relatively lower correlation than non-EMG artifacts. When CCA is used to remove EMG artifacts from EEG signals, the components whose correlation value is lower than the threshold are identified as EMG artifacts, and then the 'clean' EEG signals can be reconstructed by the remnant components. The experimental results show that CCA outperforms ICA for removing EMG artifacts. Moreover, combining with the presented threshold, CCA enables to effectively remove EMG artifacts with little distortion of the underlying brain activity signals in real-time.