TY  - JOUR
N1  - cited By 200
TI  - Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques
SP  - 139
AV  - none
EP  - 149
PB  - Kluwer Academic Publishers
SN  - 01589938
N2  - This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive taskâ??Ravenâ??s advance progressive metric test and (2) the EEG signals recorded in rest conditionâ??eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98  by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53â??3.06 and 3.06â??6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate. © 2015, Australasian College of Physical Scientists and Engineers in Medicine.
IS  - 1
ID  - scholars5987
KW  - Artificial intelligence; Classification (of information); Discrete wavelet transforms; Electroencephalography; Electrophysiology; Extraction; Feature extraction; Learning systems; Nearest neighbor search; Statistical tests; Wavelet decomposition; Wavelet transforms
KW  -  Approximation coefficients; Cognitive task; Complex cognitive tasks; Feature extraction and classification; K-nearest neighbor classifier; Machine learning techniques; Multi layer perceptron; Wavelet energy feature
KW  -  Biomedical signal processing
KW  -  accuracy; adult; Article; cognition; comparative study; controlled study; electroencephalogram; human; human experiment; k nearest neighbor; learning algorithm; machine learning; male; mathematical computing; normal human; priority journal; psychometry; quantitative analysis; Raven advance progressive metric; sensitivity and specificity; support vector machine; task performance; validation study; wavelet analysis; classification; electroencephalography; physiology; reproducibility; young adult
KW  -  Adult; Cognition; Electroencephalography; Humans; Machine Learning; Male; Reproducibility of Results; Task Performance and Analysis; Wavelet Analysis; Young Adult
JF  - Australasian Physical and Engineering Sciences in Medicine
A1  - Amin, H.U.
A1  - Malik, A.S.
A1  - Ahmad, R.F.
A1  - Badruddin, N.
A1  - Kamel, N.
A1  - Hussain, M.
A1  - Chooi, W.-T.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84930642013&doi=10.1007%2fs13246-015-0333-x&partnerID=40&md5=e0b9477182d677ba9c5d03e8d2a558c3
VL  - 38
Y1  - 2015///
ER  -