title: EEG classification by learning vector quantization.
abstract: EEG classification using Learning Vector Quantization (LVQ) is introduced on the basis of a Brain-Computer Interface (BCI) built in Graz, where a subject controlled a cursor in one dimension on a monitor using potentials recorded from the intact scalp. The method of classification with LVQ is described in detail along with first results on a subject who participated in four on-line cursor control sessions. Using this data, extensive off-line experiments were performed to show the influence of the various parameters of the classifier and the extracted features of the EEG on the classification results.
authors.affiliation: Department of Medical Informatics, Graz University of Technology.
authors.forename: D
authors.initials: D
authors.lastname: Flotzinger
authors.forename: J
authors.initials: J
authors.lastname: Kalcher
authors.forename: G
authors.initials: G
authors.lastname: Pfurtscheller
mesh_terms: D000465:Algorithms; D004292:Dominance, Cerebral / Q000502:physiology; D004569:Electroencephalography / Q000145:classification* / Q000295:instrumentation; D006801:Humans; D009044:Motor Cortex / Q000502:physiology; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D013003:Somatosensory Cortex / Q000502:physiology; D014584:User-Computer Interface