title: TVAR modeling of EEG to detect audio distraction during simulated driving.
abstract: OBJECTIVE
  The objective of our current study was to look for the EEG correlates that can reveal the engaged state of the brain while undertaking cognitive tasks. Specifically, we aimed to identify EEG features that could detect audio distraction during simulated driving.
  
  
  METHODS
  Time varying autoregressive (TVAR) analysis using Kalman smoother was carried out on short time epochs of EEG data collected from participants as they undertook two simulated driving tasks. TVAR coefficients were then used to construct all pole model enabling the identification of EEG features that could differentiate normal driving from audio distracted driving.
  
  
  RESULTS
  Pole analysis of the TVAR model led to the visualization of event related synchronization/desynchronization (ERS/ERD) patterns in the form of pole displacements in pole plots of the temporal EEG channels in the z plane enabling the differentiation of the two driving conditions. ERS in the EEG data has been demonstrated during audio distraction as an associated phenomenon.
  
  
  CONCLUSIONS
  Visualizing the ERD/ERS phenomenon in terms of pole displacement is a novel approach. Although ERS/ERD has previously been demonstrated as reliable when applied to motor related tasks, it is believed to be the first time that it has been applied to investigate human cognitive phenomena such as attention and distraction. Results confirmed that distracted/non-distracted driving states can be identified using this approach supporting its applicability to cognition research.
authors.affiliation: Cognitive Neuro-Engineering Laboratory, Division of IT, Engineering and Environment, University of South Australia, Adelaide, Australia.
authors.forename: Nabaraj
authors.initials: N
authors.lastname: Dahal
authors.forename: D Nanda
authors.initials: DN
authors.lastname: Nandagopal
authors.forename: Bernadine
authors.initials: B
authors.lastname: Cocks
authors.forename: Ramasamy
authors.initials: R
authors.lastname: Vijayalakshmi
authors.forename: Naga
authors.initials: N
authors.lastname: Dasari
authors.forename: Paul
authors.initials: P
authors.lastname: Gaertner
mesh_terms: D000328:Adult; D000465:Algorithms; D001288:Attention / Q000502:physiology*; D001307:Auditory Perception / Q000502:physiology*; D001334:Automobile Driving; D003071:Cognition / Q000502:physiology; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010470:Perceptual Masking / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014796:Visual Perception / Q000502:physiology*; D055815:Young Adult