title: On-line EEG classification during externally-paced hand movements using a neural network-based classifier.
abstract: EEGs of 6 normal subjects were recorded during sequences of periodic left or right hand movement. Left or right was indicated by a visual cue. The question posed was: 'Is it possible to move a cursor on a monitor to the right or left side using the EEG signals for cursor control?' For this purpose the EEG during performance of hand movement was analyzed and classified on-line. A neural network in form of a learning vector quantizertion (LVQ) with an input dimension of 16 was trained to classify EEG patterns from two electrodes and two time windows. After two training sessions on 2 different days, 4 subjects showed a classification accuracy of 89-100%. For two subjects classification was not possible. These results show that in general movement specific EEG-patterns can be found, classified in real time and used to move a cursor on a monitor to the left or right. On-line EEG classification is necessary when the EEG is used as input signal to a brain computer interface (BCI). Such a BCI can be a help for handicapped people.
authors.affiliation: Ludwig Boltzmann-Institute of Medical Informatics and Neuroinformatics, University of Technology, Graz, Austria.
authors.forename: G
authors.initials: G
authors.lastname: Pfurtscheller
authors.forename: J
authors.initials: J
authors.lastname: Kalcher
authors.forename: C
authors.initials: C
authors.lastname: Neuper
authors.forename: D
authors.initials: D
authors.lastname: Flotzinger
authors.forename: M
authors.initials: M
authors.lastname: Pregenzer
mesh_terms: D000328:Adult; D004569:Electroencephalography; D005260:Female; D006225:Hand / Q000502:physiology*; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology*; D016571:Neural Networks, Computer