title: Simultaneous real-time monitoring of multiple cortical systems.
abstract: OBJECTIVE
  Real-time monitoring of the brain is potentially valuable for performance monitoring, communication, training or rehabilitation. In natural situations, the brain performs a complex mix of various sensory, motor or cognitive functions. Thus, real-time brain monitoring would be most valuable if (a) it could decode information from multiple brain systems simultaneously, and (b) this decoding of each brain system were robust to variations in the activity of other (unrelated) brain systems. Previous studies showed that it is possible to decode some information from different brain systems in retrospect and/or in isolation. In our study, we set out to determine whether it is possible to simultaneously decode important information about a user from different brain systems in real time, and to evaluate the impact of concurrent activity in different brain systems on decoding performance.
  
  
  METHODS
  We study these questions using electrocorticographic signals recorded in humans. We first document procedures for generating stable decoding models given little training data, and then report their use for offline and for real-time decoding from 12 subjects (six for offline parameter optimization, six for online experimentation). The subjects engage in tasks that involve movement intention, movement execution and auditory functions, separately, and then simultaneously. Main Results: Our real-time results demonstrate that our system can identify intention and movement periods in single trials with an accuracy of 80.4% and 86.8%, respectively (where 50% would be expected by chance). Simultaneously, the decoding of the power envelope of an auditory stimulus resulted in an average correlation coefficient of 0.37 between the actual and decoded power envelopes. These decoders were trained separately and executed simultaneously in real time.
  
  
  CONCLUSIONS
  This study yielded the first demonstration that it is possible to decode simultaneously the functional activity of multiple independent brain systems. Our comparison of univariate and multivariate decoding strategies, and our analysis of the influence of their decoding parameters, provides benchmarks and guidelines for future research on this topic.
authors: Wadsworth Center, New York State Department of Health, Albany, NY, USA. Department of Neurology, Albany Medical College, Albany, NY, USA. Early Brain Injury Recovery Program, Burke-Cornell Medical Research Institute, White Plains, NY, USA.
authors: Disha
authors: D
authors: Gupta
authors: N
authors: N
authors: Jeremy Hill
authors: Peter
authors: P
authors: Brunner
authors: Aysegul
authors: A
authors: Gunduz
authors: Anthony L
authors: AL
authors: Ritaccio
authors: Gerwin
authors: G
authors: Schalk
mesh_terms: D000328:Adult; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000503:physiopathology*; D003198:Computer Simulation; D003199:Computer Systems; D004562:Electrocardiography / Q000379:methods*; D004827:Epilepsy / Q000503:physiopathology*; D005071:Evoked Potentials; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D009415:Nerve Net / Q000503:physiopathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity