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Common Methodology for Cardiac and Ocular Artifact Suppression from EEG Recordings by Combining Ensemble Empirical Mode Decomposition with Regression Approach

机译:通过整合经验模式分解与回归方法相结合的EEG记录抑制心脏和眼部伪像的通用方法

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摘要

Electroencephalography (EEG) is a non-invasive way of recording brain activities, making it useful for diagnosing various neurological disorders. However, artifact signals associated with eye blinks or the heart spread across the scalp, contaminating EEG recordings and making EEG data analysis difficult. To solve this problem, we implement a common methodology to suppress both cardiac and ocular artifact signal, by correlating the measured contaminated EEG signals with the clean reference electro-oculography (EOG) and electrocardiography (EKG) data and subtracting the scaled EOG and EKG from the contaminated EEG recording. In the proposed methodology, the clean EOG and EKG signals are extracted by subjecting the raw reference time-series data to ensemble empirical mode decomposition to obtain the intrinsic mode functions. Then, an unsupervised technique is used to capture the artifact components. We compare the distortion introduced into the brain signal after artifact suppression using the proposed method with those obtained using conventional regression alone and with a wavelet-based approach. The results show that the proposed method outperforms the other techniques, with an additional advantage of being a common methodology for the suppression of two types of artifact.
机译:脑电图(EEG)是记录脑部活动的一种非侵入性方式,使其可用于诊断各种神经系统疾病。然而,与眨眼有关的伪像信号或心脏遍布头皮散布,污染了脑电图记录,使脑电数据分析变得困难。为了解决这个问题,我们通过将测量的污染的EEG信号与干净的参考眼电图(EOG)和心电图(EKG)数据相关联并从中减去缩放后的EOG和EKG,来实施一种抑制心脏和眼部伪影信号的通用方法被污染的脑电图记录。在所提出的方法中,通过对原始参考时间序列数据进行整体经验模式分解以获得固有模式函数,从而提取干净的EOG和EKG信号。然后,使用无监督技术来捕获伪像成分。我们使用拟议的方法将伪影抑制后引入脑信号中的失真与仅使用常规回归以及基于小波的方法获得的失真进行比较。结果表明,所提出的方法优于其他技术,具有作为抑制两种伪影的通用方法的另一个优势。

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