Annals of Biomedical Engineering

, Volume 36, Issue 3, pp 467–475

Muscle Artifact Removal from Human Sleep EEG by Using Independent Component Analysis

Authors

  • Maite Crespo-Garcia
    • Laboratory of Functional NeuroscienceUniversity Pablo de Olavide
  • Mercedes Atienza
    • Laboratory of Functional NeuroscienceUniversity Pablo de Olavide
    • Laboratory of Functional NeuroscienceUniversity Pablo de Olavide
Article

DOI: 10.1007/s10439-008-9442-y

Cite this article as:
Crespo-Garcia, M., Atienza, M. & Cantero, J.L. Ann Biomed Eng (2008) 36: 467. doi:10.1007/s10439-008-9442-y

Abstract

Muscle artifacts are typically associated with sleep arousals and awakenings in normal and pathological sleep, contaminating EEG recordings and distorting quantitative EEG results. Most EEG correction techniques focus on ocular artifacts but little research has been done on removing muscle activity from sleep EEG recordings. The present study was aimed at assessing the performance of four independent component analysis (ICA) algorithms (AMUSE, SOBI, Infomax, and JADE) to separate myogenic activity from EEG during sleep, in order to determine the optimal method. AMUSE, Infomax, and SOBI performed significantly better than JADE at eliminating muscle artifacts over temporal regions, but AMUSE was independent of the signal-to-noise ratio over non-temporal regions and markedly faster than the remaining algorithms. AMUSE was further successful at separating muscle artifacts from spontaneous EEG arousals when applied on a real case during different sleep stages. The low computational cost of AMUSE, and its excellent performance with EEG arousals from different sleep stages supports this ICA algorithm as a valid choice to minimize the influence of muscle artifacts on human sleep EEG recordings.

Keywords

Muscle artifactsSleepArousalsAwakeningsEEGIndependent component analysisBlind source separation techniques

Copyright information

© Biomedical Engineering Society 2008