Abstract
Motor imagery (MI) tasks evoke event-related desynchronization (ERD) and synchronization (ERS); the ERD-/ERS-related features appearing at specific channels are frequency and time localized. Therefore, optimal channels, frequency band and time interval are of great significance for MI electroencephalography feature extraction. In this paper, channel selection method based on linear discriminant criteria is used to automatically select the channels with high discriminative powers. In addition, the concept of artificial bee colony algorithm is first introduced to find the global optimal combination of frequency band and time interval simultaneously without prior knowledge for common spatial pattern features extraction and classification. Experimental results demonstrate that this scheme can adapt to user-specific patterns and find the relatively optimal channels, frequency band and time interval for feature extraction. The classification results on the BCI Competition III Dataset IVa and BCI Competition IV Dataset IIa clearly present the effectiveness of the proposed method outperforming most of the other competing methods in the literature.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
