title: Single-trial connectivity estimation for classification of motor imagery data.
abstract: OBJECTIVE
  Many brain-computer interfaces (BCIs) use band power (BP) changes in the electroencephalogram to distinguish between different motor imagery (MI) patterns. Most current approaches do not take connectivity of separated brain areas into account. Our objective is to introduce single-trial connectivity features and apply these features to BCI data.
  
  
  METHODS
  We introduce a procedure for extracting single-trial connectivity estimates from vector autoregressive (VAR) models of independent components in a BCI setting.
  
  
  RESULTS
  In a simulated BCI, we demonstrate that the directed transfer function (DTF) with full-frequency normalization and the direct DTF give classification results similar to BP, while other measures such as the partial directed coherence perform significantly worse.
  
  
  CONCLUSIONS
  We show that single-trial MI classification is possible with connectivity measures extracted from VAR models, and that a BCI could potentially utilize such measures.
authors.affiliation: Institute for Knowledge Discovery, Graz University of Technology, Graz, Austria.
authors.forename: Martin
authors.initials: M
authors.lastname: Billinger
authors.forename: Clemens
authors.initials: C
authors.lastname: Brunner
authors.forename: Gernot R
authors.initials: GR
authors.lastname: Müller-Putz
mesh_terms: D000328:Adult; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology; D005260:Female; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008297:Male; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity