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Related Experiment Video

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An automatic pre-processing pipeline for EEG analysis (APP) based on robust statistics.

Janir Ramos da Cruz1, Vitaly Chicherov2, Michael H Herzog2

  • 1Institute for Systems and Robotics - Lisbon (LARSys) and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Portugal; Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Switzerland.

Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology
|May 7, 2018
PubMed
Summary

A new automatic pipeline (APP) efficiently removes electroencephalography (EEG) artifacts, performing comparably to expert review and outperforming other automated methods for both event-related potential (ERP) and resting-state (RS) EEG data.

Keywords:
Automatic pre-processingERPElectroencephalographyResting-state

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Supervised artifact rejection in electroencephalography (EEG) is time-consuming, especially with large datasets.
  • High-density EEG and large participant numbers necessitate efficient automated pre-processing methods.

Purpose of the Study:

  • To introduce a novel automatic pipeline (APP) for EEG pre-processing and artifact rejection.
  • To evaluate APP's performance against existing automatic methods and expert supervised pre-processing.

Main Methods:

  • APP was tested on event-related potential (ERP) and resting-state (RS) EEG data.
  • Performance was compared with FASTER (ERP) and TAPEEG/Prep pipeline (RS), as well as expert manual review.
  • The pipeline incorporates robust statistics for enhanced artifact detection.

Main Results:

  • APP rejected fewer bad channels and epochs compared to other automated methods.
  • For ERP data, APP yielded higher amplitudes than FASTER, aligning with supervised results.
  • For RS data, APP's spectral measures correlated well with both automated and supervised methods.

Conclusions:

  • The proposed automatic pipeline (APP) effectively removes EEG artifacts.
  • APP performs comparably to supervised artifact rejection and surpasses existing automated alternatives.
  • APP offers a reliable and efficient solution for pre-processing large-scale EEG datasets.