relation: https://khub.utp.edu.my/scholars/17454/
title: Real-time Efficacy of Features Extraction using Machine Learning and Deep Learning for Frontal Alpha Asymmetry.
creator: Hafeez, Y.
creator: Ali, S.S.A.
creator: Amin, H.U.
creator: Naqvi, S.F.
creator: Adil, S.H.
creator: Boon, T.T.
description: 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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2022
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Hafeez, Y. and Ali, S.S.A. and Amin, H.U. and Naqvi, S.F. and Adil, S.H. and Boon, T.T.  (2022) Real-time Efficacy of Features Extraction using Machine Learning and Deep Learning for Frontal Alpha Asymmetry.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141583599&doi=10.1109%2fROMA55875.2022.9915702&partnerID=40&md5=029f1a8776cea4b53656759864dcc9d4
relation: 10.1109/ROMA55875.2022.9915702
identifier: 10.1109/ROMA55875.2022.9915702