Classification of cognitive and resting states of the brain using EEG features

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.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Impact Factor: cited By 8
Uncontrolled 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)
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 25 Mar 2022 07:38
Last Modified: 25 Mar 2022 07:38
URI: http://scholars.utp.edu.my/id/eprint/30829

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