title: Action prediction based on anticipatory brain potentials during simulated driving.
abstract: OBJECTIVE
  The ability of an automobile to infer the driver's upcoming actions directly from neural signals could enrich the interaction of the car with its driver. Intelligent vehicles fitted with an on-board brain-computer interface able to decode the driver's intentions can use this information to improve the driving experience. In this study we investigate the neural signatures of anticipation of specific actions, namely braking and accelerating.
  
  
  METHODS
  We investigated anticipatory slow cortical potentials in electroencephalogram recorded from 18 healthy participants in a driving simulator using a variant of the contingent negative variation (CNV) paradigm with Go and No-go conditions: count-down numbers followed by 'Start'/'Stop' cue. We report decoding performance before the action onset using a quadratic discriminant analysis classifier based on temporal features.
  
  
  RESULTS
  (i) Despite the visual and driving related cognitive distractions, we show the presence of anticipatory event related potentials locked to the stimuli onset similar to the widely reported CNV signal (with an average peak value of -8 μV at electrode Cz). (ii) We demonstrate the discrimination between cases requiring to perform an action upon imperative subsequent stimulus (Go condition, e.g. a 'Red' traffic light) versus events that do not require such action (No-go condition; e.g. a 'Yellow' light); with an average single trial classification performance of 0.83 ± 0.13 for braking and 0.79 ± 0.12 for accelerating (area under the curve). (iii) We show that the centro-medial anticipatory potentials are observed as early as 320 ± 200 ms before the action with a detection rate of 0.77 ± 0.12 in offline analysis.
  
  
  CONCLUSIONS
  We show for the first time the feasibility of predicting the driver's intention through decoding anticipatory related potentials during simulated car driving with high recognition rates.
authors.affiliation: Defitech Chair in Brain-Machine Interface, Center for Neuroprosthetics, Institute of Bioengineering and School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech H4, 1202, Geneva, Switzerland.
authors.forename: Zahra
authors.initials: Z
authors.lastname: Khaliliardali
authors.forename: Ricardo
authors.initials: R
authors.lastname: Chavarriaga
authors.forename: Lucian Andrei
authors.initials: LA
authors.lastname: Gheorghe
authors.forename: José del R
authors.initials: Jdel R
authors.lastname: Millán
mesh_terms: D000328:Adult; D001334:Automobile Driving / Q000523:psychology; D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces / Q000523:psychology; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods; D005071:Evoked Potentials / Q000502:physiology*; D005260:Female; D005544:Forecasting; D006801:Humans; D033182:Intention; D008297:Male; D055815:Young Adult