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[Methods for Processing Physiological Artifacts in Single/Few-Channel EEG Signals].

Guojing Wang1,2, Hongyun Liu2, Weidong Wang2

  • 1School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191.

Zhongguo Yi Liao Qi Xie Za Zhi = Chinese Journal of Medical Instrumentation
|June 12, 2024
PubMed
Summary
This summary is machine-generated.

This review covers methods to remove physiological artifacts from single/few-channel electroencephalogram (EEG) recordings. It analyzes various techniques and their suitability for different scenarios to improve EEG data quality.

Keywords:
artifactmachine learningmixed methodsingle/few-channel EEG

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Electroencephalogram (EEG) is a crucial non-invasive technique for measuring brain electrical activity.
  • The increasing use of single/few-channel EEG systems is hindered by physiological artifacts that compromise data analysis and application.
  • Artifacts in EEG signals can stem from various physiological sources, distorting the true neural information.

Purpose of the Study:

  • To comprehensively review existing methods for physiological artifact removal in single/few-channel EEG.
  • To analyze and summarize hybrid artifact removal strategies tailored for specific scenarios (e.g., single vs. multi-artifact, online vs. offline processing).
  • To discuss validation metrics and trends in single/few-channel EEG artifact processing.

Main Methods:

  • Review of regression and filtering techniques for artifact removal.
  • Analysis of decomposition methods (e.g., Independent Component Analysis) for source separation.
  • Evaluation of machine learning approaches applied to EEG artifact detection and correction.

Main Results:

  • Identified various established and emerging methods for physiological artifact removal in EEG.
  • Categorized hybrid artifact removal approaches based on signal characteristics and application context.
  • Reviewed performance validation metrics for semi-simulated and real EEG data.

Conclusions:

  • Effective artifact removal is critical for the reliable application of single/few-channel EEG.
  • Hybrid methods offer promising solutions for diverse artifact scenarios and processing requirements.
  • Further research into artifact processing is essential for advancing single/few-channel EEG applications.