TY  - JOUR
ID  - scholars7156
KW  - accuracy; Article; classification algorithm; cognition; electrode; electroencephalogram; epilepsy; event related potential; human; intelligence test; k nearest neighbor; noise; pattern recognition; priority journal; recall; seizure; support vector machine; algorithm; brain mapping; cluster analysis; data mining; electroencephalogram; electroencephalography; physiology; procedures; reproducibility; statistics and numerical data
KW  -  Algorithms; Brain Mapping; Brain Waves; Cluster Analysis; Data Mining; Electroencephalography; Humans; Reproducibility of Results; Support Vector Machine
N2  - Feature extraction and classification for electroencephalogram (EEG) in medical applications is a challenging task. The EEG signals produce a huge amount of redundant data or repeating information. This redundancy causes potential hurdles in EEG analysis. Hence, we propose to use this redundant information of EEG as a feature to discriminate and classify different EEG datasets. In this study, we have proposed a JPEG2000 based approach for computing data redundancy from multi-channels EEG signals and have used the redundancy as a feature for classification of EEG signals by applying support vector machine, multi-layer perceptron and k-nearest neighbors classifiers. The approach is validated on three EEG datasets and achieved high accuracy rate (95â??99 ) in the classification. Dataset-1 includes the EEG signals recorded during fluid intelligence test, dataset-2 consists of EEG signals recorded during memory recall test, and dataset-3 has epileptic seizure and non-seizure EEG. The findings demonstrate that the approach has the ability to extract robust feature and classify the EEG signals in various applications including clinical as well as normal EEG patterns. © 2015, Springer Science+Business Media New York.
IS  - 2
Y1  - 2016///
VL  - 29
JF  - Brain Topography
A1  - Amin, H.U.
A1  - Malik, A.S.
A1  - Kamel, N.
A1  - Hussain, M.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958179056&doi=10.1007%2fs10548-015-0462-2&partnerID=40&md5=6afa613922c2fa52086f957534875753
AV  - none
SP  - 207
TI  - A Novel Approach Based on Data Redundancy for Feature Extraction of EEG Signals
N1  - cited By 27
PB  - Springer New York LLC
SN  - 08960267
EP  - 217
ER  -