title: Leveraging anatomical information to improve transfer learning in brain-computer interfaces.
abstract: OBJECTIVE
  Brain-computer interfaces (BCIs) represent a technology with the potential to rehabilitate a range of traumatic and degenerative nervous system conditions but require a time-consuming training process to calibrate. An area of BCI research known as transfer learning is aimed at accelerating training by recycling previously recorded training data across sessions or subjects. Training data, however, is typically transferred from one electrode configuration to another without taking individual head anatomy or electrode positioning into account, which may underutilize the recycled data.
  
  
  METHODS
  We explore transfer learning with the use of source imaging, which estimates neural activity in the cortex. Transferring estimates of cortical activity, in contrast to scalp recordings, provides a way to compensate for variability in electrode positioning and head morphologies across subjects and sessions.
  
  
  RESULTS
  Based on simulated and measured electroencephalography activity, we trained a classifier using data transferred exclusively from other subjects and achieved accuracies that were comparable to or surpassed a benchmark classifier (representative of a real-world BCI). Our results indicate that classification improvements depend on the number of trials transferred and the cortical region of interest.
  
  
  CONCLUSIONS
  These findings suggest that cortical source-based transfer learning is a principled method to transfer data that improves BCI classification performance and provides a path to reduce BCI calibration time.
authors.affiliation: Graduate Program in Neuroscience University of Washington, Box 357270, Seattle, WA 98195, USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Wronkiewicz
authors.forename: Eric
authors.initials: E
authors.lastname: Larson
authors.forename: Adrian K C
authors.initials: AK
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D062207:Brain-Computer Interfaces; D002540:Cerebral Cortex / Q000033:anatomy & histology* / Q000502:physiology*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D000069550:Machine Learning; D008953:Models, Anatomic; D008959:Models, Neurological; D010363:Pattern Recognition, Automated / Q000379:methods