title: Predicting motor learning performance from Electroencephalographic data.
abstract: BACKGROUND
  Research on the neurophysiological correlates of visuomotor integration and learning (VMIL) has largely focused on identifying learning-induced activity changes in cortical areas during motor execution. While such studies have generated valuable insights into the neural basis of VMIL, little is known about the processes that represent the current state of VMIL independently of motor execution. Here, we present empirical evidence that a subject's performance in a 3D reaching task can be predicted on a trial-to-trial basis from pre-trial electroencephalographic (EEG) data. This evidence provides novel insights into the brain states that support successful VMIL.
  
  
  METHODS
  Six healthy subjects, attached to a seven degrees-of-freedom (DoF) robot with their right arm, practiced 3D reaching movements in a virtual space, while an EEG recorded their brain's electromagnetic field. A random forest ensemble classifier was used to predict the next trial's performance, as measured by the time needed to reach the goal, from pre-trial data using a leave-one-subject-out cross-validation procedure.
  
  
  RESULTS
  The learned models successfully generalized to novel subjects. An analysis of the brain regions, on which the models based their predictions, revealed areas matching prevalent motor learning models. In these brain areas, the α/μ frequency band (8-14 Hz) was found to be most relevant for performance prediction.
  
  
  CONCLUSIONS
  VMIL induces changes in cortical processes that extend beyond motor execution, indicating a more complex role of these processes than previously assumed. Our results further suggest that the capability of subjects to modulate their α/μ bandpower in brain regions associated with motor learning may be related to performance in VMIL. Accordingly, training subjects in α/μ-modulation, e.g., by means of a brain-computer interface (BCI), may have a beneficial impact on VMIL.
authors.affiliation: Department Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany. tmeyer@tuebingen.mpg.de.
authors.forename: Timm
authors.initials: T
authors.lastname: Meyer
authors.forename: Jan
authors.initials: J
authors.lastname: Peters
authors.forename: Thorsten O
authors.initials: TO
authors.lastname: Zander
authors.forename: Bernhard
authors.initials: B
authors.lastname: Schölkopf
authors.forename: Moritz
authors.initials: M
authors.lastname: Grosse-Wentrup
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D005260:Female; D006801:Humans; D007858:Learning / Q000502:physiology*; D008297:Male; D009043:Motor Activity / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*