eprintid: 7181
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/71/81
datestamp: 2023-11-09 16:18:58
lastmod: 2023-11-09 16:18:58
status_changed: 2023-11-09 16:08:41
type: conference_item
metadata_visibility: show
creators_name: Ahmad, R.F.
creators_name: Malik, A.S.
creators_name: Amin, H.U.
creators_name: Kamel, N.
creators_name: Qayyum, A.
creators_name: Reza, F.
title: Nonlinear features based classification of active and resting states of human brain using EEG
ispublished: pub
keywords: Biomedical signal processing; Brain; Electric variables measurement; Electroencephalography; Electrophysiology; Entropy; Extraction; Feature extraction; Image processing; Low noise amplifiers; Neuroimaging; Neurons, Approximate entropy; CPEI; Electrical signal; Linear feature; Neural activity; Nonlinear features; Permutation entropy; Sample entropy, Classification (of information)
note: cited By 0; Conference of 4th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2015 ; Conference Date: 19 October 2015 Through 21 October 2015; Conference Code:119504
abstract: Electroencephalography is most common noninvasive neuroimaging modality and it is widely used for measuring brain electrical signals. Measurement of electrical signals from the scalp requires high density electrodes and low noise amplifier. It is well known fact that neural activity increased with increasing the mental work e.g., IQ task in our case. In this paper, non-linear features have been used to classify the active and resting states of the human brain. We have used EEG acquired from 08 healthy participants during IQ task and resting conditions. Nonlinear feature e.g., Approximate entropy, sample entropy and Composite permutation entropy index (CPEI) have been computed from recorded EEG data. These nonlinear features were fed to the classifier and we are able to classify the active and rest conditions. Also for classification, SVM produced better results with 89.1 and 92.5 accuracy for eyes open (EO) vs IQ and eyes open (EO) vs eyes close (EC) conditions respectively as compared to other classifiers. Also results compared with linear features extraction methods. © 2015 IEEE.
date: 2016
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971654910&doi=10.1109%2fICSIPA.2015.7412201&partnerID=40&md5=e9218363ad34a8f566478751190bedcf
id_number: 10.1109/ICSIPA.2015.7412201
full_text_status: none
publication: IEEE 2015 International Conference on Signal and Image Processing Applications, ICSIPA 2015 - Proceedings
pagerange: 264-268
refereed: TRUE
isbn: 9781479989966
citation:   Ahmad, R.F. and Malik, A.S. and Amin, H.U. and Kamel, N. and Qayyum, A. and Reza, F.  (2016) Nonlinear features based classification of active and resting states of human brain using EEG.  In: UNSPECIFIED.