A preliminary study of muscular artifact cancellation in single-channel EEG.

Xun Chen,Rabab Ward,Aiping Liu,Hu Peng

doi:10.3390/s141018370

Abstract

Electroencephalogram (EEG) recordings are often contaminated with muscular artifacts that strongly obscure the EEG signals and complicates their analysis. For the conventional case, where the EEG recordings are obtained simultaneously over many EEG channels, there exists a considerable range of methods for removing muscular artifacts. In recent years, there has been an increasing trend to use EEG information in ambulatory healthcare and related physiological signal monitoring systems. For practical reasons, a single EEG channel system must be used in these situations. Unfortunately, there exist few studies for muscular artifact cancellation in single-channel EEG recordings. To address this issue, in this preliminary study, we propose a simple, yet effective, method to achieve the muscular artifact cancellation for the single-channel EEG case. This method is a combination of the ensemble empirical mode decomposition (EEMD) and the joint blind source separation (JBSS) techniques. We also conduct a study that compares and investigates all possible single-channel solutions and demonstrate the performance of these methods using numerical simulations and real-life applications. The proposed method is shown to significantly outperform all other methods. It can successfully remove muscular artifacts without altering the underlying EEG activity. It is thus a promising tool for use in ambulatory healthcare systems.

Highlights

The electroencephalogram (EEG) signals are often contaminated by various physiological activities of non-interest, such as the electrocardiogram (ECG), electrooculogram (EOG) and electromyogram (EMG)
The emerging joint blind source separation (BSS) (JBSS) techniques are formulated to separate the muscle artifacts from the multidimensional datasets obtained in the first step
There exist two independent components (ICs)-based methods for performing source separation of a single-channel signal, they are found unsuitable for removing the artifacts arising from muscle activity

Summary

Introduction

The electroencephalogram (EEG) signals are often contaminated by various physiological activities of non-interest, such as the electrocardiogram (ECG), electrooculogram (EOG) and electromyogram (EMG). While ECG and EOG artifacts can be effectively removed by using adaptive filters and blind source separation (BSS) techniques [1], the artifacts induced by muscular activity (e.g., biting, chewing and frowning) are difficult to correct [2]. The main reason lies in the fact that EMG artifacts have a higher amplitude than the EEG signals, a wide spectral distribution and a variable topographical distribution [2]. These muscular artifacts obscure the EEG signals and make EEG interpretation extremely complicated or almost impossible [3]. ICA utilizes higher-order statistics to separate the EEG recordings into statistically independent components (ICs). One possible reason is that ICA only exploits the spatial structure of source signals

Methods

Results

Conclusion