Unsupervised movement onset detection from EEG recorded during self-paced real hand movement.
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).
Awwad Shiekh Hasan
Bashar
B
BCI Group, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK. bawwad@essex.ac.uk
Gan
John Q
JQ
D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D004569:Electroencephalography / Q000379:methods; D005260:Female; D006225:Hand / Q000502:physiology*; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology*; D011597:Psychomotor Performance; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface