Abstract
The stress is the major problem that occurs in daily life, which affects on the physical and mental health. There are various methods for detection of stress. In this paper, EEG signal analysis is used for stress recognition. The output of a neurosky mindwave mobile 2 sensor is waves like alpha, beta, and gamma in a specific range. By analyzing these values and keeping a threshold, the dataset formation occurs, and further to train the data, artificial neural network technique (RBFN algorithm) is used. The system learns and is trained using RBFN. The states (stress) are detected. The work is tested on hundred cases and found 80% accurate.
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Wakale, A., Verma, U. (2021). Brain Activity Analysis for Stress Recognition. In: Merchant, S.N., Warhade, K., Adhikari, D. (eds) Advances in Signal and Data Processing . Lecture Notes in Electrical Engineering, vol 703. Springer, Singapore. https://doi.org/10.1007/978-981-15-8391-9_46
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DOI: https://doi.org/10.1007/978-981-15-8391-9_46
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