title: Detection of braking intention in diverse situations during simulated driving based on EEG feature combination.
abstract: OBJECTIVE
  We developed a simulated driving environment for studying neural correlates of emergency braking in diversified driving situations. We further investigated to what extent these neural correlates can be used to detect a participant's braking intention prior to the behavioral response.
  
  
  METHODS
  We measured electroencephalographic (EEG) and electromyographic signals during simulated driving. Fifteen participants drove a virtual vehicle and were exposed to several kinds of traffic situations in a simulator system, while EEG signals were measured. After that, we extracted characteristic features to categorize whether the driver intended to brake or not.
  
  
  RESULTS
  Our system shows excellent detection performance in a broad range of possible emergency situations. In particular, we were able to distinguish three different kinds of emergency situations (sudden stop of a preceding vehicle, sudden cutting-in of a vehicle from the side and unexpected appearance of a pedestrian) from non-emergency (soft) braking situations, as well as from situations in which no braking was required, but the sensory stimulation was similar to stimulations inducing an emergency situation (e.g., the sudden stop of a vehicle on a neighboring lane).
  
  
  CONCLUSIONS
  We proposed a novel feature combination comprising movement-related potentials such as the readiness potential, event-related desynchronization features besides the event-related potentials (ERP) features used in a previous study. The performance of predicting braking intention based on our proposed feature combination was superior compared to using only ERP features. Our study suggests that emergency situations are characterized by specific neural patterns of sensory perception and processing, as well as motor preparation and execution, which can be utilized by neurotechnology based braking assistance systems.
authors.affiliation: Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-ku, Seoul 136-713, Korea.
authors.forename: Il-Hwa
authors.initials: IH
authors.lastname: Kim
authors.forename: Jeong-Woo
authors.initials: JW
authors.lastname: Kim
authors.forename: Stefan
authors.initials: S
authors.lastname: Haufe
authors.forename: Seong-Whan
authors.initials: SW
authors.lastname: Lee
mesh_terms: D000328:Adult; D001334:Automobile Driving; D003657:Decision Making / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D033182:Intention; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D011597:Psychomotor Performance / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface