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Title |
Voting Classifier based Model for Mental Stress Detection and Classification Using EEG Signal |
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Author |
Navdeep Shakya, Rahul Dubey, Laxmi Shrivastava |
| Citation |
Vol. 24 No. 11 pp. 113-118 |
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Abstract |
According to World Health Origination (WHO), stress is a major problem of human beings which have a large effect on physical and mental health. The state of emotional tension from adverse or demanding circumstances is called stress. It can be experienced by each person in regular lifestyle due to job, some family problems, and other personal issues. Some kind of stress is important for completing the task but a lot of stress causes harm to human health. Hence, nowadays, identification of stress levels is important. This paper proposes one of the simplest methodology for the detection of stress by the analysis of EEG signal. Fast fourier transform (FFT) is used in the proposed method to generate the power spectral density (PSD) vector. PSD vector is used as an input to the voting classifier. Voting classifier is a combination of K-nearest neighbour (KNN), and random forest (RF). The proposed method achieves the highest classification accuracy of 88%. |
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Keywords |
Mental Stress, EEG, KNN, Random Forest, Voting classifier. |
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