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EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback System.

Chao Chen1,2, Xuecong Yu1, Abdelkader Nasreddine Belkacem3

  • 1Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, 300384 China.

Journal of Medical and Biological Engineering
|February 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an objective method for anxiety disorder detection using electroencephalography (EEG) signals. Our neurofeedback approach achieved high accuracy in classifying anxiety states, paving the way for better diagnostic tools.

Keywords:
Affective BCIAnxiety stateMulti-class EEG classificationNeurofeedback

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Biomedical Engineering

Background:

  • Anxiety disorders are prevalent psychiatric conditions characterized by extreme fear and worry.
  • Current anxiety assessment relies on subjective questionnaires, lacking objective diagnostic standards.
  • Neural changes associated with anxiety offer a potential avenue for objective evaluation.

Purpose of the Study:

  • To develop an objective method for identifying and classifying anxiety states using electroencephalography (EEG) signals.
  • To investigate neural changes associated with anxiety through a neurofeedback experiment.
  • To establish a basis for an affective Brain-Computer Interface (BCI) for anxiety disorder detection.

Main Methods:

  • A closed neurofeedback experiment with three stages was conducted on 34 subjects.
  • Electroencephalography (EEG) resting state and mindfulness signals were recorded.
  • Visual Analogue Scale (VAS) scores were used to classify subjects into non-anxiety, moderate, or severe anxiety groups.

Main Results:

  • Support Vector Machine (SVM) classifiers accurately distinguished between non-anxiety and anxiety states using Power Spectral Density (PSD) patterns.
  • Classification accuracies reached up to 92.48% (Gaussian kernel) and 88.60% (polynomial kernel).
  • High average accuracies were observed for healthy (95.31%) and anxiety (87.18%) subjects.

Conclusions:

  • The proposed EEG neurofeedback-based classification approach is effective for anxiety disorder detection.
  • This method provides an objective and efficient means for evaluating anxiety states.
  • The findings support the development of affective BCI systems for psychiatric disorder assessment.