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Medical & Biological Engineering & Computing

Erschienen in:

01.03.2010 | Original Article

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

verfasst von: Bashar Awwad Shiekh Hasan, John Q. Gan

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 3/2010

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Abstract

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).

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Titel: Unsupervised movement onset detection from EEG recorded during self-paced real hand movement
verfasst von: Bashar Awwad Shiekh Hasan
John Q. Gan
Publikationsdatum: 01.03.2010
Verlag: Springer-Verlag
Erschienen in: Medical & Biological Engineering & Computing / Ausgabe 3/2010
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI: https://doi.org/10.1007/s11517-009-0550-0

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