Classifying EEG-based motor imagery tasks by means of time-frequency synthesized spatial patterns

Tao Wang, Jie Deng, Bin He

Research output: Contribution to journalArticlepeer-review

143 Scopus citations

Abstract

To develop 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 electroencephalogram (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 forms the spatial patterns for a given electrode montage at a time-frequency grid. Time-frequency weights determined by training process are used to synthesize the contributions from the time-frequency domains. The present method was tested in nine human subjects performing left or right hand movement imagery tasks. The overall classification accuracies for nine human subjects were about 80% in the 10-fold cross-validation, without rejecting any trials from the dataset. The loci of MI activity were shown in the spatial topography of differential-mode patterns over the sensorimotor area. The present method does not contain a priori subject-dependent parameters, and is computationally efficient. The testing results are promising considering the fact that no trials are excluded due to noise or artifact. The present method promises to provide a useful alternative as a general purpose classification procedure for MI classification.

Original languageEnglish (US)
Pages (from-to)2744-2753
Number of pages10
JournalClinical Neurophysiology
Volume115
Issue number12
DOIs
StatePublished - Dec 1 2004

Keywords

  • Brain-computer interface (BCI)
  • Electroencephalography (EEG)
  • Event-related desynchronization (ERD)
  • Motor imagery
  • Spatial correlation
  • Time-frequency weighting

ASJC Scopus subject areas

  • Sensory Systems
  • Neurology
  • Clinical Neurology
  • Physiology (medical)

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