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Research Article

Mental Stress Classification from Brain Signals using MLP Classifier

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  • @ARTICLE{10.4108/eetpht.9.4341,
        author={Soumya Samarpita and Rabinarayan Satpathy and Pradipta Kumar Mishra and Aditya Narayan Panda},
        title={Mental Stress Classification from Brain Signals using MLP Classifier},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={9},
        number={1},
        publisher={EAI},
        journal_a={PHAT},
        year={2023},
        month={11},
        keywords={Mental stress, Electroencephalogram, EEG, Healthcare, Classification, Multi-layer Perceptron, MLP, Brain Signal},
        doi={10.4108/eetpht.9.4341}
    }
    
  • Soumya Samarpita
    Rabinarayan Satpathy
    Pradipta Kumar Mishra
    Aditya Narayan Panda
    Year: 2023
    Mental Stress Classification from Brain Signals using MLP Classifier
    PHAT
    EAI
    DOI: 10.4108/eetpht.9.4341
Soumya Samarpita1,*, Rabinarayan Satpathy1, Pradipta Kumar Mishra1, Aditya Narayan Panda1
  • 1: Sri Sri University
*Contact email: soumya.s2020-21ds@srisrsiuniversity.edu.in

Abstract

INTRODUCTION: The most common and widespread mental condition that unavoidably affects people's mood and conduct is stress. The physiological reaction to powerful emotional, intellectual, and physical obstacles might be viewed as stress. As a result, early stress detection can result in solutions for potential improvements and ultimate event suppression. OBJECTIVES: To classify mental stress from the EEG signals of humans using an MLP classifier. METHODS: We examine the EEG signal analysis techniques currently in use for detecting mental stress using Multi-layer Perceptron (MLP). RESULTS: The suggested technique has a 95% classification accuracy performance. CONCLUSION: In our study, the use of MLP classifiers for stress detection from EEG signals has shown promising results. The high accuracy and precision of the classifiers, as well as the informative nature of certain EEG frequency bands, suggest that this approach could be a valuable tool for stress detection and management.

Keywords
Mental stress, Electroencephalogram, EEG, Healthcare, Classification, Multi-layer Perceptron, MLP, Brain Signal
Received
2023-09-05
Accepted
2023-11-01
Published
2023-11-09
Publisher
EAI
http://dx.doi.org/10.4108/eetpht.9.4341

Copyright © 2023 S. Samarpita et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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