Classification of motor imagery EEG patterns and their topographic representation

Tao Wang*, Jie Deng, Bin He

*Corresponding author for this work

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

We have developed a single trial motor imagery (MI) classification strategy for the brain computer interface (BCI) applications by using time-frequency synthesis approach to accommodate the individual difference, and using the spatial patterns derived from EEG rhythmic components as the feature description. The EEGs are decomposed into a series of frequency bands, and the instantaneous power is represented by the envelop of oscillatory activity, which formed the spatial patterns for a given electrode montage at a time-frequency grid. Time-frequency weights determined by training process were used to synthesize the contributions at the time-frequency domains. The overall classification accuracies for three selected human subjects performing left or right hand movement imagery tasks, were about 87 percent in the ten-fold cross validation without rejecting trials. The loci of motor imagery activity were shown in the spatial topography of differential mode patterns over the sensorimotor area. The present method promises to provide a useful alternative as a general purpose classification procedure for motor imagery classification.

Original languageEnglish (US)
Pages (from-to)4359-4362
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume26 VI
StatePublished - Dec 1 2004

Keywords

  • Brain computer interface
  • Event-related desynchronization
  • Motor Imagery
  • Spatial correlation

ASJC Scopus subject areas

  • Bioengineering

Fingerprint Dive into the research topics of 'Classification of motor imagery EEG patterns and their topographic representation'. Together they form a unique fingerprint.

  • Cite this