title: Prediction of performance level during a cognitive task from ongoing EEG oscillatory activities.
abstract: OBJECTIVE
  Tracking the level of performance in cognitive tasks may be useful in environments, such as aircraft, in which the awareness of the pilots is critical for security. In this paper, the usefulness of EEG for the prediction of performance is investigated.
  
  
  METHODS
  We present a new methodology that combines various ongoing EEG measurements to predict performance level during a cognitive task. We propose a voting approach that combines the outputs of elementary support vector machine (SVM) classifiers derived from various sets of EEG parameters in different frequency bands. The spectral power and phase synchrony of the oscillatory activities are used to classify the periods of rapid reaction time (RT) versus the slow RT responses of each subject.
  
  
  RESULTS
  The voting algorithm significantly outperforms classical SVM and gives a good average classification accuracy across 12 subjects (71%) and an average information transfer rate (ITR) of 0.49bit/min. The main discriminating activities are laterally distributed theta power and anterio-posterior alpha synchronies, possibly reflecting the role of a visual-attentional network in performance.
  
  
  CONCLUSIONS
  Power and synchrony measurements enable the discrimination between periods of high average reaction time versus periods of low average reaction time in a same subject. Moreover, the proposed approach is easy to interpret as it combines various types of measurements for classification, emphasizing the most informative.
  
  
  CONCLUSIONS
  Ongoing EEG recordings can predict the level of performance during a cognitive task. This can lead to real-time EEG monitoring devices for the anticipation of human mistakes.
authors.affiliation: Laboratoire Neurosciences Cognitives et Imagerie Cérébrale, CNRS UPR 640-LENA, 75013 Paris Cedex 13, France.
authors.forename: Michel
authors.initials: M
authors.lastname: Besserve
authors.forename: Matthieu
authors.initials: M
authors.lastname: Philippe
authors.forename: Geneviève
authors.initials: G
authors.lastname: Florence
authors.forename: François
authors.initials: F
authors.lastname: Laurent
authors.forename: Line
authors.initials: L
authors.lastname: Garnero
authors.forename: Jacques
authors.initials: J
authors.lastname: Martinerie
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D003071:Cognition / Q000502:physiology*; D004569:Electroencephalography; D006801:Humans; D013647:Task Performance and Analysis