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Unsupervised movement onset detection from EEG recorded during self-paced real hand movement

Awwad Shiekh Hasan, B and Gan, JQ (2010) 'Unsupervised movement onset detection from EEG recorded during self-paced real hand movement.' Medical and Biological Engineering and Computing, 48 (3). 245 - 253. ISSN 0140-0118

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This article presents an unsupervised method for movement onset detection from electroencephalography (EEG) signals recorded during self-paced real hand movement. A Gaussian Mixture Model (GMM) is used to model the movement and idle-related EEG data. The GMM built along with appropriate classification and post processing methods are used to detect movement onsets using self-paced EEG signals recorded from five subjects, achieving True-False rate difference between 63 and 98%. The results show significant performance enhancement using the proposed unsupervised method, both in the sample-by-sample classification accuracy and the event-by-event performance, in comparison with the state-of-the-art supervised methods. The effectiveness of the proposed method suggests its potential application in self-paced Brain-Computer Interfaces (BCI). © 2009 International Federation for Medical and Biological Engineering.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
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: 17 Oct 2012 11:23
Last Modified: 23 Jan 2019 00:15

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