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BUSA Deep Learning Model for EEG Signal Analysis

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Abstract

Depression, a debilitating mental illness, profoundly impacts an individual’s cognition, behaviour, and emotions. Despite efforts to quantify depression rates through electro-encephalo-gram (EEG) signal analysis, classification remains challenging due to inherent noise. This paper introduces a Bat based UNET signal analysis, aimed at accurately classifying depression rates using a normal EEG dataset. It comprises of pre-processing, feature extraction, feature selection, and classification stages. The framework excels at noise reduction during pre-processing, enhancing dataset integrity. Feature extraction leverages band power and correlation dimension to extract crucial features. Furthermore, feature selection optimizes classification accuracy by refining the fitness function of bats in the classification layer. Utilizing a standardized EEG dataset implemented in Matrix Laboratory (MATLAB), the proposed technique demonstrates superior performance compared to existing methods, as evidenced by metrics such as accuracy, area under the curve, precision, and recall (or sensitivity). This innovative framework represents a significant advancement in the classification of depression rates.

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The software application developed by the present authors is available.

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Code availability is ensured by the corresponding author.

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Authors and Affiliations

  1. National Institute of Technology, Ravangla, Sikkim, India

    Varun Gupta

  2. Amity University in Tashkent, Tashkent, Uzbekistan

    Danish Ather

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  1. Varun Gupta
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All simulations and methodology were carried out by the corresponding author. Literature survey was conducted collaboratively by all authors.

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Correspondence to Varun Gupta.

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Gupta, V., Ather, D. BUSA Deep Learning Model for EEG Signal Analysis. Wireless Pers Commun 136, 2521–2543 (2024). https://doi.org/10.1007/s11277-024-11409-4

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