EEG classification by learning vector quantization

Biomed Tech (Berl). 1992 Dec;37(12):303-9. doi: 10.1515/bmte.1992.37.12.303.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Dominance, Cerebral / physiology
  • Electroencephalography / classification*
  • Electroencephalography / instrumentation
  • Humans
  • Motor Cortex / physiology
  • Neural Networks, Computer
  • Signal Processing, Computer-Assisted / instrumentation*
  • Somatosensory Cortex / physiology
  • User-Computer Interface*