eprintid: 7156
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/71/56
datestamp: 2023-11-09 16:18:57
lastmod: 2023-11-09 16:18:57
status_changed: 2023-11-09 16:08:37
type: article
metadata_visibility: show
creators_name: Amin, H.U.
creators_name: Malik, A.S.
creators_name: Kamel, N.
creators_name: Hussain, M.
title: A Novel Approach Based on Data Redundancy for Feature Extraction of EEG Signals
ispublished: pub
keywords: 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, Algorithms; Brain Mapping; Brain Waves; Cluster Analysis; Data Mining; Electroencephalography; Humans; Reproducibility of Results; Support Vector Machine
note: cited By 27
abstract: 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 (95�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.
date: 2016
publisher: Springer New York LLC
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958179056&doi=10.1007%2fs10548-015-0462-2&partnerID=40&md5=6afa613922c2fa52086f957534875753
id_number: 10.1007/s10548-015-0462-2
full_text_status: none
publication: Brain Topography
volume: 29
number: 2
pagerange: 207-217
refereed: TRUE
issn: 08960267
citation:   Amin, H.U. and Malik, A.S. and Kamel, N. and Hussain, M.  (2016) A Novel Approach Based on Data Redundancy for Feature Extraction of EEG Signals.  Brain Topography, 29 (2).  pp. 207-217.  ISSN 08960267