Original Article

Medical & Biological Engineering & Computing

, Volume 48, Issue 3, pp 245-253

First online:

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

  • Bashar Awwad Shiekh HasanAffiliated withBCI Group, University of Essex Email author 
  • , John Q. GanAffiliated withBCI Group, University of Essex

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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).


Movement onset detection Electroencephalography Self-paced BCI Gaussian Mixture Models Unsupervised learning Post processing