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EEG-based mild depressive detection using feature selection methods and classifiers.

Xiaowei Li¹, Bin Hu¹, Shuting Sun¹

¹School of Information Science and Engineering, Lanzhou University, Lanzhou, China.

Computer Methods and Programs in Biomedicine

|October 1, 2016

Summary

This summary is machine-generated.

This study shows that combining Greedy Stepwise (GSW) feature selection with k-nearest neighbor (KNN) classification accurately detects mild depression using Electroencephalography (EEG) beta band data. Simplified EEG systems show promise for portable depression detection.

Keywords:

Classification algorithms EEG Mild depression Search methods

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Area of Science:

Neuroscience
Computational Psychiatry
Biomedical Engineering

Background:

Mild depression poses a significant global health challenge.
Accurate detection of mild depression requires advanced technological tools like Electroencephalography (EEG).

Purpose of the Study:

To identify key EEG frequency bands and brain regions associated with mild depression.
To determine optimal feature selection and classification methods for mild depression detection using EEG.

Main Methods:

Collected 128-channel EEG data from 37 university students during a facial expression viewing task.
Employed BayesNet, SVM, LR, KNN, and RF classifiers with CFS-based feature selection methods (BF, GSW, GS, LFS, RS).
Utilized Independent Samples T-test with Bonferroni correction to identify significant electrodes and features.

Main Results:

The combination of Greedy Stepwise (GSW) feature selection and k-nearest neighbor (KNN) classifier achieved high accuracy (up to 98%) and AUC (up to 0.997) in the beta frequency band.
A simplified EEG system using only five electrodes (FP1, FP2, F3, O2, T3) with linear features also demonstrated high performance.
Selected features by GSW were validated using T-tests.

Conclusions:

GSW + KNN classification shows promising results for mild depression detection, outperforming previous studies.
The left parietotemporal lobe in the beta EEG frequency band is crucial for detecting mild depression.
A reduced set of EEG channels and linear features are suitable for portable mild depression detection systems.