TY  - CONF
SN  - 9781467378154
N2  - 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.
Y1  - 2016///
TI  - A statistical analysis on learning and non-learning mental states using EEG
KW  - Biomedical engineering; Discrete wavelet transforms; Fast Fourier transforms; Feature extraction; Power spectral density; Spectral density; Spectrum analysis; Statistical methods; Students; Wavelet transforms
KW  -  2D animation; 2D Multimedia animationsg; Cognitive loads; Data collection; Electro-encephalogram (EEG); Mental state; Spectral analysis method; Spectral feature extraction
KW  -  Electroencephalography
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84965152097&doi=10.1109%2fISSBES.2015.7435889&partnerID=40&md5=1cd4c95147bb23acdfd1c5cd97fc652f
N1  - cited By 3; Conference of IEEE Student Symposium in Biomedical Engineering and Sciences, ISSBES 2015 ; Conference Date: 4 November 2015; Conference Code:120053
A1  - Mazher, M.
A1  - Aziz, A.A.
A1  - Malik, A.S.
A1  - Qayyum, A.
ID  - scholars7116
EP  - 40
SP  - 36
PB  - Institute of Electrical and Electronics Engineers Inc.
AV  - none
ER  -