%K Deep learning; Extraction; Learning systems; Physiological models; Physiology, Deep learning; Features extraction; Machine-learning; Mental stress; Mental stress detection; Physiological signals; Physiological state; Psycho-physiological; Real- time; Stress detection, Feature extraction
%I Institute of Electrical and Electronics Engineers Inc.
%O cited By 0; Conference of 5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022 ; Conference Date: 6 August 0202 Through 8 August 0202; Conference Code:183507
%X The frontal alpha asymmetry represents as the neuromarker for stress. Stress is the psycho-physiological state of brain in response to some event or a demand. The continuous monitoring of mental stress is necessary to avoid chronic health issues. The real-time monitoring of frontal alpha asymmetry is necessary in daily life and to help in the therapy for example neurofeedback. In this paper, different approaches of machine learning and deep learning were adopted to extract the frontal alpha asymmetry features. The results analysis was based on the efficacy and the comparison of techniques for feature extraction has also been presented. © 2022 IEEE.
%D 2022
%L scholars17454
%A Y. Hafeez
%A S.S.A. Ali
%A H.U. Amin
%A S.F. Naqvi
%A S.H. Adil
%A T.T. Boon
%T Real-time Efficacy of Features Extraction using Machine Learning and Deep Learning for Frontal Alpha Asymmetry.
%J 2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation, ROMA 2022
%R 10.1109/ROMA55875.2022.9915702