title: Quantifying the time for accurate EEG decoding of single value-based decisions.
abstract: BACKGROUND
  Recent neuroimaging studies suggest that value-based decision-making may rely on mechanisms of evidence accumulation. However no studies have explicitly investigated the time when single decisions are taken based on such an accumulation process.
  
  
  METHODS
  Here, we outline a novel electroencephalography (EEG) decoding technique which is based on accumulating the probability of appearance of prototypical voltage topographies and can be used for predicting subjects' decisions. We use this approach for studying the time-course of single decisions, during a task where subjects were asked to compare reward vs. loss points for accepting or rejecting offers.
  
  
  RESULTS
  We show that based on this new method, we can accurately decode decisions for the majority of the subjects. The typical time-period for accurate decoding was modulated by task difficulty on a trial-by-trial basis. Typical latencies of when decisions are made were detected at ∼500 ms for 'easy' vs. ∼700 ms for 'hard' decisions, well before subjects' response (∼340 ms). Importantly, this decision time correlated with the drift rates of a diffusion model, evaluated independently at the behavioral level.
  
  
  CONCLUSIONS
  We compare the performance of our algorithm with logistic regression and support vector machine and show that we obtain significant results for a higher number of subjects than with these two approaches. We also carry out analyses at the average event-related potential level, for comparison with previous studies on decision-making.
  
  
  CONCLUSIONS
  We present a novel approach for studying the timing of value-based decision-making, by accumulating patterns of topographic EEG activity at single-trial level.
authors.affiliation: Electroencephalography Brain Mapping Core, Center for Biomedical Imaging (CIBM), Department of Radiology, University Hospital Center, University of Lausanne, 1011 Lausanne, Switzerland; Laboratoire de recherche en Neuroimagerie - LREN, Department of Clinical Neuroscience, Lausanne University and University Hospital, 1011 Lausanne, Switzerland. Electronic address: Athina.Tzovara@uzh.ch.
authors.forename: Athina
authors.initials: A
authors.lastname: Tzovara
authors.affiliation: Chair in Non-Invasive Brain-Computer Interface, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
authors.forename: Ricardo
authors.initials: R
authors.lastname: Chavarriaga
authors.affiliation: Electroencephalography Brain Mapping Core, Center for Biomedical Imaging (CIBM), Department of Radiology, University Hospital Center, University of Lausanne, 1011 Lausanne, Switzerland; Laboratoire de recherche en Neuroimagerie - LREN, Department of Clinical Neuroscience, Lausanne University and University Hospital, 1011 Lausanne, Switzerland.
authors.forename: Marzia
authors.initials: M
authors.lastname: De Lucia
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D003657:Decision Making / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials; D005260:Female; D005715:Gambling; D006801:Humans; D016015:Logistic Models; D008297:Male; D008960:Models, Psychological; D015999:Multivariate Analysis; D009483:Neuropsychological Tests; D011597:Psychomotor Performance / Q000502:physiology; D011930:Reaction Time / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D013997:Time Factors; D014796:Visual Perception / Q000502:physiology
keywords: Accumulation; Decision-making; Decoding; Drift diffusion model; EEG; Single-trial