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Classification of EEG signals: An interpretable approach using functional data analysis.

Yuyan Yi1, Nedret Billor1, Mingli Liang2

  • 1Department of Mathematics and Statistics, Auburn University, USA.

Journal of Neuroscience Methods
|April 28, 2022
PubMed
Summary

This study introduces a novel three-stage algorithm using functional data analysis to interpret electroencephalography (EEG) signals. The method effectively classifies human behaviors from EEG data, enhancing understanding of brain activity.

Keywords:
Functional data analysisGroup LASSOInterpretable classificationPenalized multiple functional logistic regressionScalp EEG

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Area of Science:

  • Neuroscience
  • Data Science
  • Biomedical Engineering

Background:

  • Electroencephalography (EEG) records brain's electrical activity noninvasively.
  • EEG data is often treated as continuous voltage flow, posing analysis challenges.
  • Interpretable analysis of EEG for behavioral classification is crucial.

Purpose of the Study:

  • To propose a novel three-stage algorithm for interpretable EEG analysis.
  • To classify human behaviors using functional data analysis techniques.
  • To enhance the understanding and application of EEG signals.

Main Methods:

  • Functional data analysis framework applied to EEG signals.
  • Wavelet transform for extracting time and frequency information.
  • Functional testing for channel and frequency selection.
  • Penalized multiple functional logistic regression for classification.

Main Results:

  • The proposed algorithm successfully classifies human behaviors from scalp EEG data.
  • The method provides an interpretable classification, detailing significant EEG features.
  • Validation through simulation and real scalp EEG data confirmed efficacy.

Conclusions:

  • The three-stage functional data analysis algorithm offers an interpretable approach to EEG-based behavior classification.
  • This method advances the utility of EEG in understanding human behavior.
  • The algorithm demonstrates the potential of functional data analysis in neuroscience.