Unsupervised movement onset detection from EEG recorded during self-paced real hand movement
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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).
KeywordsMovement onset detection Electroencephalography Self-paced BCI Gaussian Mixture Models Unsupervised learning Post processing
The authors would like to thank C.S.L. Tsui for providing the data sets and the source code for LDA and naive Bayesian classifiers. This work is part of the project “Adaptive Asynchronous Brain Actuated Control” funded by UK EPSRC. The first author’s study is funded by Aga Khan Foundation (AKF).
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