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Temporal modeling of EEG during self-paced hand movement and its application in onset detection

Hasan, BAS and Gan, JQ (2011) 'Temporal modeling of EEG during self-paced hand movement and its application in onset detection.' Journal of Neural Engineering, 8 (5). ISSN 1741-2560

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Abstract

The temporal behavior of electroencephalography (EEG) recorded during self-paced hand movement is investigated for the purpose of improving EEG classification in general and onset detection in particular. Four temporal models based on conditional random fields are developed and applied to classify EEG data into the movement or idle class. They are further used for building an onset detection system and tested on self-paced EEG signals recorded from five subjects. True-false rates ranging from 74% to 98% have been achieved on different subjects, with significant improvement over non-temporal methods. The effectiveness of the proposed methods suggests their potential use in self-paced brain-computer interfaces. © 2011 IOP Publishing Ltd.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Users 161 not found.
Date Deposited: 18 Oct 2012 10:09
Last Modified: 23 Jan 2019 00:15
URI: http://repository.essex.ac.uk/id/eprint/4074

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