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Analysis of region of interest (RoI) of brain for detection of depression using EEG signal

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Abstract

Depression is a psychiatric disorder that has a negative impact on a person’s development, such as how they think, feel, and behave. Electroencephalogram (EEG) signal can act as biomarker for detection of depression. In this study, analysis is carried out on EEG signals to identify in which region of the brain depression affects so that high accuracy with low number of EEG channels could be obtained.

For this purpose the brain is divided basically into six regions. Four EEG band power features - delta, theta, alpha and beta band power and non-linear features - Approximate Entropy (ApEn), Sample Entropy (SampEn), Correlation Dimension (CD) and Detrended Fluctuation Analysis (DFA) are extracted from all the channels. Based on different regions of the brain and different features, classifiers - Linear Discriminant Analysis (LDA), Naïve Bayesian (NB), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and Bagging have been used.

Results show non-linear feature SampEn and DFA showed comparatively higher accuracy compared to 4 EEG band power features, CD and ApEn. Highest classification accuracy of 95.23% was attained using SVM along with ReliefF. Results also show that depression effects the temporal region of the brain.

Thus a portable device with lesser number of channels specifically placed in temporal region would be able to detect depression with high accuracy which could act as an adjunct tool for detection of depression.

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Data availability

Data used is a Publicly available dataset [9].

Code availability

Authors would make the code available whenever required for evaluation by the editors and reviewers.

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Acknowledgments

The authors would like to thank Mumtaz et al. for the dataset contribution [33]. The authors would also like to thank Dr. Rakesh Kumar Sinha, Professor and Head, Bioengineering and Biotechnology, Birla Institute of Technology (BIT), Mesra, India for always supporting us with his immense knowledge and expertise.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Information Technology (IIIT), Ranchi, Jharkhand, India

    Shalini Mahato

  2. Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India

    Sanchita Paul

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Correspondence to Shalini Mahato.

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Mahato, S., Paul, S. Analysis of region of interest (RoI) of brain for detection of depression using EEG signal. Multimed Tools Appl 83, 763–786 (2024). https://doi.org/10.1007/s11042-023-15827-7

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