NDLI: Single-Trial EEG Classification Using Logistic Regression Based on Ensemble Synchronization

Content Provider	IEEE Xplore Digital Library
Author	Prasad, P.D. Halahalli, H.N. John, J.P. Majumdar, K.K.
Copyright Year	2013
Abstract	In this paper, we propose an ensemble synchronization measure across all EEG channel pairs of a cluster based on Frobenius norm of the phase synchronization matrix, in a 0-1 scale enabling a direct comparison between clusters with different number of channels. Using this metric, we studied the intrahemispheric EEG synchronization in the lower gamma band (30-40 Hz) during 1229 single trials of an audio-visual integration cross modal task (CMT) recorded from five patients with schizophrenia and five healthy control subjects. Using ensemble synchronization measure and response latency of single trials recorded during the CMT as features for logistic regression, we could classify each single trial of EEG as belonging to a patient with schizophrenia or a healthy control subject with 73% accuracy, with an area under receiver operating characteristics curve of 0.83. We also propose a likelihood rating to denote the possibility of a subject belonging to the schizophrenia group.
Page Count	7
File Size	985496
Starting Page	1074
Ending Page	1080
File Format	PDF
ISSN	21682194
Volume Number	18
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Synchronization Electroencephalography Accuracy Informatics Visualization Training phase synchronization Cross modal task (CMT) Frobenius norm Hilbert transform logistic regression multichannel measure
Content Type	Text
Resource Type	Article
Subject	Health Information Management Health Informatics Electrical and Electronic Engineering Computer Science Applications Biotechnology

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