title: Frequency component selection for an ECoG-based brain-computer interface.
abstract: The aim of the present study was to investigate the most significant frequency components in electrocorticogram (ECoG) recordings in order to operate a brain computer interface (BCI). For this purpose the time-frequency ERD/ERS map and the distinction sensitive learning vector quantization (DSLVQ) are applied to ECoG from three subjects, recorded during a self-paced finger movement. The results show that the ERD/ERS pattern found in ECoG generally matches the ERD/ERS pattern found in EEG recordings, but has an increased prevalence of frequency components in the beta range.
authors.affiliation: Department of Medical Informatics, Institute of Biomedical Engineering, Graz University of Technology, Austria. Reinhold.Scherer@TUGraz.at
authors.forename: R
authors.initials: R
authors.lastname: Scherer
authors.forename: B
authors.initials: B
authors.lastname: Graimann
authors.forename: J E
authors.initials: JE
authors.lastname: Huggins
authors.forename: S P
authors.initials: SP
authors.lastname: Levine
authors.forename: G
authors.initials: G
authors.lastname: Pfurtscheller
mesh_terms: D001931:Brain Mapping; D002540:Cerebral Cortex / Q000503:physiopathology*; D003344:Cortical Synchronization; D004569:Electroencephalography / Q000295:instrumentation*; D004576:Electromyography / Q000295:instrumentation; D004827:Epilepsy / Q000503:physiopathology / Q000601:surgery; D005071:Evoked Potentials / Q000502:physiology; D006801:Humans; D009043:Motor Activity / Q000502:physiology; D009415:Nerve Net / Q000503:physiopathology*; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D014584:User-Computer Interface