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

作者：Hasan Bashar Awwad Shiekh^*; Gan John Q

来源：MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2010, 48(3): 245-253.

DOI：10.1007/s11517-009-0550-0

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

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).

• 出版日期2010-3