eprintid: 7116
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/71/16
datestamp: 2023-11-09 16:18:55
lastmod: 2023-11-09 16:18:55
status_changed: 2023-11-09 16:08:31
type: conference_item
metadata_visibility: show
creators_name: Mazher, M.
creators_name: Aziz, A.A.
creators_name: Malik, A.S.
creators_name: Qayyum, A.
title: A statistical analysis on learning and non-learning mental states using EEG
ispublished: pub
keywords: Biomedical engineering; Discrete wavelet transforms; Fast Fourier transforms; Feature extraction; Power spectral density; Spectral density; Spectrum analysis; Statistical methods; Students; Wavelet transforms, 2D animation; 2D Multimedia animationsg; Cognitive loads; Data collection; Electro-encephalogram (EEG); Mental state; Spectral analysis method; Spectral feature extraction, Electroencephalography
note: cited By 3; Conference of IEEE Student Symposium in Biomedical Engineering and Sciences, ISSBES 2015 ; Conference Date: 4 November 2015; Conference Code:120053
abstract: This study is based on statistical analyses of leaning and non-learning mental states based on electroencephalogram (EEG) recorded brain waves. This work draw a comparison on two spectral feature extraction techniques fast Fourier transform (FFT) and discrete wavelet transform (DWT). 10 subjects are used for data collection using 7 electrodes. A 2D animation based presentation is used as a stimulus for learning state. Power spectral density feature is derived for four EEG recorded brain waves delta, theta, alpha and beta using FFT and DWT. The results comparisons of ANOVA statistical test indicate that alpha brain wave has more discriminative behavior from non-learning to learning mental state than other waves. Also these results illustrate that DWT is better spectral analysis method than FFT. © 2015 IEEE.
date: 2016
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84965152097&doi=10.1109%2fISSBES.2015.7435889&partnerID=40&md5=1cd4c95147bb23acdfd1c5cd97fc652f
id_number: 10.1109/ISSBES.2015.7435889
full_text_status: none
publication: ISSBES 2015 - IEEE Student Symposium in Biomedical Engineering and Sciences: By the Student for the Student
pagerange: 36-40
refereed: TRUE
isbn: 9781467378154
citation:   Mazher, M. and Aziz, A.A. and Malik, A.S. and Qayyum, A.  (2016) A statistical analysis on learning and non-learning mental states using EEG.  In: UNSPECIFIED.