Modelling EEG Dataset for Stress State Recognition using Decision Tree Approach

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Mamata S Kalas, B. F. Momin

Abstract

Electroencephalography (EEG) is a predominant tool for learning the stress behavior. This work concentrates towards stress detection by means of eye states. This work proposes a framework which would be supportive in identifying human stress level and as an outcome, distinguishes a normal or stressed person. In this work, we used decision trees, carried out the performance analysis and found that it gives good performance in recognizing the stress states. This analysis is performed with reference to eye state: whether eyes are closed indicating rest, open eyes with blinks.

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How to Cite
, M. S. K. B. F. M. (2018). Modelling EEG Dataset for Stress State Recognition using Decision Tree Approach. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 4(4), 82–88. Retrieved from https://ijfrcsce.org/index.php/ijfrcsce/article/view/1473
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