%0 Conference Paper
%A Mazher, M.
%A Aziz, A.A.
%A Malik, A.S.
%A Qayyum, A.
%D 2016
%F scholars:7116
%I Institute of Electrical and Electronics Engineers Inc.
%K 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
%P 36-40
%R 10.1109/ISSBES.2015.7435889
%T A statistical analysis on learning and non-learning mental states using EEG
%U https://khub.utp.edu.my/scholars/7116/
%X 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.
%Z cited By 3; Conference of IEEE Student Symposium in Biomedical Engineering and Sciences, ISSBES 2015 ; Conference Date: 4 November 2015; Conference Code:120053