eprintid: 6885
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/68/85
datestamp: 2023-11-09 16:18:41
lastmod: 2023-11-09 16:18:41
status_changed: 2023-11-09 16:07:55
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: Reza, F.
title: Classification of cognitive and resting states of the brain using EEG features
ispublished: pub
keywords: Brain; Data acquisition; Electroencephalography; Entropy; Extraction; Feature extraction; Support vector machines, Approximate entropy; Classification accuracy; Cognitive loads; Cognitive state; Complex dynamics; CPEI; Nonlinear features; Sample entropy, Classification (of information)
note: cited By 12; Conference of 11th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2016 ; Conference Date: 15 May 2016 Through 18 May 2016; Conference Code:123196
abstract: Human brain is considered as complex system having different mental states e.g., rest, active or cognitive states. It is well understood fact that brain activity increases with the cognitive load. This paper describes the cognitive and resting state classification based on EEG features. Previously, most of the studies used linear features. EEG signals are non-stationary in nature and have complex dynamics which is not fully mapped by linear methods. Here, we used non-linear feature extraction methods to classify the cognitive and resting states of the human brain. Data acquisition were carried out on eight healthy participants during cognitive state i.e., IQ task and rest conditions i.e., eyes open. After preprocessing, EEG features were extracted using both linear as well as non-linear. Further, these features were passed to the classifier. Results showed that with support vector machine (SVM), we achieved 87.5 classification accuracy with linear and 92.1 classification accuracy with non-linear features. © 2016 IEEE.
date: 2016
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984998023&doi=10.1109%2fMeMeA.2016.7533741&partnerID=40&md5=c5a6c71e0fbcdcee5771416e14a83961
id_number: 10.1109/MeMeA.2016.7533741
full_text_status: none
publication: 2016 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2016 - Proceedings
refereed: TRUE
isbn: 9781467391726
citation:   Ahmad, R.F. and Malik, A.S. and Amin, H.U. and Kamel, N. and Reza, F.  (2016) Classification of cognitive and resting states of the brain using EEG features.  In: UNSPECIFIED.