Key result
EEG signal analysis using Support Vector Machine (SVM) classifiers achieved the maximum accuracy for stress detection among the tested models.
Why the study?
Does EEG signal analysis using classification techniques accurately detect stress?
Does EEG signal analysis using classification techniques accurately detect stress?
EEG signal analysis using SVM classifiers can accurately detect stress, suggesting potential utility for clinical intervention and prevention of mental health problems.
Physiological monitoring may aid stress identification in professionals; leaves open whether it improves outcomes or warrants adoption.
Stress is the root cause of every mental problem. There are various ways for the occurrence of stress which has proven to give a negative impact on the human body. Hence, stress has a severe impact in the life of a working professional due to higher expectations from the management, time managerial issues, and monetary terms. The stress is a supporter of depression and anxiety risks, left ignored for a long span of time. The physiological parameters helps to identify the stress related issues. In this paper we have used the brain signals for the investigation of stress. The eeg signals are precise, accurate and reliable for the investigation purpose. The main interruption we have obtained using this test is the sweating, temperature of the body and an increase in the anxiety level. Although, EEG shares a good compatibility among the stress signals. We have compiled a detailed analysis among the different classifiers, SVM has obtained the maximum accuracy. The outcome feasibility of using the eeg for stress detection and suitable for the clinical intervention and prevention of physical and mental health problems.
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Sharma et al. (2020) studied Stress. EEG signal analysis using Support Vector Machine (SVM) vs. Other classifiers was evaluated on Stress detection accuracy. EEG signal analysis using Support Vector Machine (SVM) classifiers achieved the maximum accuracy for stress detection among the tested models.
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