Work

Selection of Neural Oscillatory Features for Human Stress Classification with Single Channel EEG Headset

Year: 2018

Type: article

Abstract: A study on classification of psychological stress in humans using electroencephalography (EEG) is presented. The stress is classified using a correlation-based feature subset selection method that efficiently reduces the feature vector length. In this study, twenty-eight participants are involved by filling in the perceived stress scale-10 (PSS-10) questionnaire and their EEG is also recorded in closed eye condition to measure the baseline stress. The recorded data is labelled on the basis of the stress level that is indicated by the participant's PSS score. The feature selection method has shown that, among the EEG oscillations, low beta, high beta, and low gamma are the most significant neural oscillations for classifying human stress. The proposed method not only reduces the time to build a classification model but also improves the classification accuracy up to 78.57% using a single channel wearable EEG device.

Source: BioMed Research International

DOI: 10.1155/2018/1049257

Authors: Sanay Muhammad Umar Saeed, Syed Muhammad Anwar, Muhammad Majid, Muhammad Awais, Majdi Alnowami

Institutions: University of Engineering and Technology Taxila, University of Surrey, King Abdulaziz University

Language: English

Topic: Emotion and Mood Recognition

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,680