Mental Stress Classification from Brain Signals using MLP Classifier
DOI:
https://doi.org/10.4108/eetpht.9.4341Keywords:
Mental stress, Electroencephalogram, EEG, Healthcare, Classification, Multi-layer Perceptron, MLP, Brain SignalAbstract
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.
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Arsalan, A., Majid, M., Butt, A. R., & Anwar, S. M. (2019). Classification of perceived mental stress using a commercially available EEG headband. IEEE journal of biomedical and health informatics, 23(6), 2257-2264.
Asif, A., Majid, M., & Anwar, S. M. (2019). Human stress classification using EEG signals in response to music tracks. Computers in biology and medicine, 107, 182-196.
Attallah, O. (2020). An effective mental stress state detection and evaluation system using minimum number of frontal brain electrodes. Diagnostics, 10(5), 292.
Bird, J. J., Manso, L. J., Ribeiro, E. P., Ekart, A., & Faria, D. R. (2018, September). A study on mental state classification using eeg-based brain-machine interface. In 2018 international conference on intelligent systems (IS) (pp. 795-800). IEEE.
Dave, S., Ambudkar, B., & Dave, N. (2022 May). Stress Analysis of Brainwave Using EEG Click.
Dimas, A. (2022). Classification of Electroencephalogram Generated by Brain for Analysis of Brain Wave Signals in Students Depression. International Journal of Engineering Technology and Natural Sciences, 4(2), 95-101.
Gaurav, A. R., & Kumar, V. (2018). EEG-metric based mental stress detection. Netw Biol, 8(1), 25-34.
Gedam, S., & Paul, S. (2021). A review on mental stress detection using wearable sensors and machine learning techniques. IEEE Access, 9, 84045-84066.
Hayashi, H., & Tsuji, T. (2022). Human–Machine Interfaces Based on Bioelectric Signals: A Narrative Review with a Novel System Proposal. IEEJ Transactions on Electrical and Electronic Engineering, 17(11), 1536-1544.
Katmah, R., Al-Shargie, F., Tariq, U., Babiloni, F., Al-Mughairbi, F., & Al-Nashash, H. (2021). A review on mental stress assessment methods using EEG signals. Sensors, 21(15), 5043.
Khosrowabadi, R., Quek, C., Ang, K. K., Tung, S. W., & Heijnen, M. (2011, July). A Brain-Computer Interface for classifying EEG correlates of chronic mental stress. In The 2011 international joint conference on neural networks (pp. 757-762). IEEE.
Lekshmi, S. S., Selvam, V., & Rajasekaran, M. P. (2014, April). EEG signal classification using principal component analysis and wavelet transform with neural network. In 2014 International Conference on Communication and Signal Processing (pp. 687-690). IEEE.
Manjunatha Siddappa, D. K. A Cognitive Approach towards Measuring Effectiveness of Meditation Using Enobio-8 EEG Device. European Journal of Molecular & Clinical Medicine, 7(08), 2020.
Rajendran, V. G., Jayalalitha, S., & Adalarasu, K. (2022). EEG Based Evaluation of Examination Stress and Test Anxiety Among College Students. Irbm, 43(5), 349-361.
Saeed, S. M. U., Anwar, S. M., Khalid, H., Majid, M., & Bagci, U. (2020). EEG based classification of long-term stress using psychological labeling. Sensors, 20(7), 1886.
Samarpita, S., & Satpathy, R. N. (2022, October). Applications of Machine Learning in Healthcare: An Overview. In 2022 1st IEEE International Conference on Industrial Electronics: Developments & Applications (ICIDeA) (pp. 51-56). IEEE.
Shakya, N., DUBEY, R., & Shrivastava, L. (2021). Stress Detection using EEG Signal Based on Fast Walsh Hadamard transform and Voting Classifier.
Sharma, R., & Chopra, K. (2020). EEG signal analysis and detection of stress using classification techniques. Journal of Information and Optimization Sciences, 41(1), 229-238.
Sharma, S., Singh, G., & Sharma, M. (2021). A comprehensive review and analysis of supervised-learning and soft computing techniques for stress diagnosis in humans. Computers in Biology and Medicine, 134, 104450.
Shaw, R., & Patra, B. K. (2022). Classifying students based on cognitive state in flipped learning pedagogy. Future Generation Computer Systems, 126, 305-317.
Suryawanshi, R., & Vanjale, S. (2023). Brain Activity Monitoring for Stress Analysis through EEG Dataset using Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 11(1s), 236-240.
Zhang, Y., Wang, Q., Chin, Z. Y., & Ang, K. K. (2020, July). Investigating different stress-relief methods using Electroencephalogram (EEG). In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 2999-3002). IEEE.
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Copyright (c) 2023 Soumya Samarpita, Rabinarayan Satpathy, Pradipta Kumar Mishra, Aditya Narayan Panda
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