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EEG Features Selection by Using Tasmanian Devil Optimization Algorithm for Stress Detection

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Evolutionary Artificial Intelligence (ICEASSM 2017)

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

Stress detection is imperative in human–machine interaction systems to monitor the mental health of the person. Various stress detection schemes based on machine learning and deep learning have been presented in the past. However, the performance of the stress detection system is challenging due to inadequate training data, poor feature variability, poor feature selection, and the intricacy of the deep learning framework. This paper provides the stress detection system using multiple electroencephalograph (EEG) signals and a Tasmanian Devil Optimization (TDO)-based feature selection scheme to select important and distinctive features. It uses a lightweight Deep Convolutional Neural Network (DCNN) to improve feature representation and stress detection. The efficiency of the suggested scheme is estimated on the public Dataset for Emotion Analysis using Physiological Signals (DEAP). It provides a stress detection rate of 97.95 and 89.75% with and without feature selection. It is observed that the proposed feature selection scheme provides a significant improvement in the traditional feature selection techniques.

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Authors and Affiliations

  1. Department of E&TC, JSPM’s Rajarshi Shahu College of Engineering, Tathawade, Pune, Maharashtra, India

    Dipali Dhake & Yogesh Angal

  2. Department of E&TC, Pimpri Chinchwad College of Engineering and Research, Ravet, Pune, Maharashtra, India

    Dipali Dhake

  3. Department of E&TC, JSPM’s Bhivarabai Sawant Institute of Technology & Research, Wagholi, Pune, Maharashtra, India

    Yogesh Angal

Authors
  1. Dipali Dhake
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  2. Yogesh Angal
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Corresponding author

Correspondence to Dipali Dhake .

Editor information

Editors and Affiliations

  1. Faculty of Innovation and Technology, Taylor’s University, Subang Jaya, Selangor, Malaysia

    David Asirvatham

  2. University of Southeast Norway, Notodden, Norway

    Francisco M. Gonzalez-Longatt

  3. Gdansk University of Technology, Gdańsk, Poland

    Przemyslaw Falkowski-Gilski

  4. Professor of Computer Engineering, Papua New Guinea University of Technology, Lae, Papua New Guinea

    R. Kanthavel

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Dhake, D., Angal, Y. (2024). EEG Features Selection by Using Tasmanian Devil Optimization Algorithm for Stress Detection. In: Asirvatham, D., Gonzalez-Longatt, F.M., Falkowski-Gilski, P., Kanthavel, R. (eds) Evolutionary Artificial Intelligence. ICEASSM 2017. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-8438-1_18

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