Toward Improved Classification of Perceived Stress Using Time Domain Features

Perceived stress is the predominant mental health concern in this age of development and progress. Timely and precise recognition of perceived stress is vital for appropriate and effective treatment. Previously, many studies intended to classify perceived stress with considerable accuracy using electroencephalography (EEG). This research study aims to accurately classify perceived stress with a lesser number of electrodes by using significant features identified by the information-gain technique. The dataset employed in this research comprises EEG signals 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 have been employed: non-overlap and overlap. After segmentation, twenty-time domain features have been extracted, and feature selection has been performed using an information-gain-based method. It has been applied to enhance feature relevance and to reduce the dimensionality of feature vectors. To label the EEG data into stressed and non-stressed groups, the Perceived Stress Scale (PSS) questionnaire has been utilized. Employing a Random Forest classifier alongside the overlap segmentation technique, our proposed method attained a maximum classification accuracy of 93.8%. This accuracy surpasses existing stress classification schemes found in the literature with a similar number of electrodes.