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Perceived stress is the predominant mental health concern in this age of development and progress. This research study aims to classify perceived stress using non-invasive electroencephalography (EEG) signals. The dataset employed in this research comprises EEG information from twenty-eight participants in a closed-eye state, utilizing commercially available Muse EEG headbands. We have preprocessed EEG data and performed analysis on EEG data spanning 210 seconds. Two segmentation techniques were employed: non-overlap and overlap. The information-gain-based feature selection method was applied before classification to extract distinct features to enhance feature relevance and reduce dimensionality. To categorize the EEG data into stressed and non-stressed groups, the Perceived Stress Scale (PSS) questionnaire was utilized. Employing a Random Forest classifier alongside an overlap segmentation technique, our proposed method attained a peak classification accuracy of 93.8%. This surpasses existing stress classification schemes found in the literature.

Towards Improved Classification of Perceived Stress using Time Domain Features