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Machine Learning Model for Computer-Aided Depression Screening among Young Adults Using Wireless EEG Headset.

Nazmus Sakib1,2, Md Kafiul Islam1,2, Tasnuva Faruk2,3

  • 1Department of Electrical and Electronic Engineering, Independent University Bangladesh (IUB), Dhaka, Bangladesh.

Computational Intelligence and Neuroscience
|June 9, 2023
PubMed
Summary

This study introduces a machine learning approach using electroencephalography (EEG) data from a wireless headset to detect depression in young adults. The method achieved high accuracy, offering a promising alternative to traditional screening tools.

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Biomedical Engineering

Background:

  • Depression significantly impacts quality of life and traditional diagnostic methods have limitations.
  • Electroencephalography (EEG) shows potential for objective depression detection.
  • Wireless EEG headsets offer a more accessible and less intrusive data collection method.

Purpose of the Study:

  • To develop and validate a machine learning model for detecting depression in young adults using wireless EEG data.
  • To evaluate the efficacy of various signal processing features and machine learning classifiers for depression identification.
  • To establish the feasibility of using consumer-grade EEG devices for mental health diagnostics.

Main Methods:

  • EEG data were collected from 32 young adults using an Emotiv Epoc+ headset.
  • Participants were screened for depression using the PHQ9 tool.
  • Features including Hjorth parameters, Shannon entropy, and Log energy entropy were extracted from filtered EEG signals (8-30 Hz) and analyzed using KNN and SVM classifiers with 5-fold cross-validation.

Main Results:

  • The proposed machine learning method achieved a high accuracy of 98.43% ± 0.15% at the AB band frequency (8-30 Hz).
  • With a 70/30 data split for training/testing, overall accuracy reached 98.10% ± 0.11%, with excellent performance across precision, sensitivity, specificity, and F1 score.
  • The findings demonstrate the effectiveness of extracted EEG features and the KNN classifier in identifying depression.

Conclusions:

  • Wireless EEG data, particularly from consumer-grade headsets like Emotiv Epoc+, can be reliably used for detecting depression in young adults.
  • The developed machine learning model, utilizing specific EEG features and classification techniques, offers a highly accurate and objective method for depression assessment.
  • This approach presents a promising, non-invasive alternative to subjective, questionnaire-based depression screening tools.