title: Predicting workload profiles of brain-robot interface and electromygraphic neurofeedback with cortical resting-state networks: personal trait or task-specific challenge?
abstract: OBJECTIVE
  Novel rehabilitation strategies apply robot-assisted exercises and neurofeedback tasks to facilitate intensive motor training. We aimed to disentangle task-specific and subject-related contributions to the perceived workload of these interventions and the related cortical activation patterns.
  
  
  METHODS
  We assessed the perceived workload with the NASA Task Load Index in twenty-one subjects who were exposed to two different feedback tasks in a cross-over design: (i) brain-robot interface (BRI) with haptic/proprioceptive feedback of sensorimotor oscillations related to motor imagery, and (ii) control of neuromuscular activity with feedback of the electromyography (EMG) of the same hand. We also used electroencephalography to examine the cortical activation patterns beforehand in resting state and during the training session of each task.
  
  
  RESULTS
  The workload profile of BRI feedback differed from EMG feedback and was particularly characterized by the experience of frustration. The frustration level was highly correlated across tasks, suggesting subject-related relevance of this workload component. Those subjects who were specifically challenged by the respective tasks could be detected by an interhemispheric alpha-band network in resting state before the training and by their sensorimotor theta-band activation pattern during the exercise.
  
  
  CONCLUSIONS
  Neurophysiological profiles in resting state and during the exercise may provide task-independent workload markers for monitoring and matching participants' ability and task difficulty of neurofeedback interventions.
authors.forename: Meike
authors.initials: M
authors.lastname: Fels
authors.forename: Robert
authors.initials: R
authors.lastname: Bauer
authors.forename: Alireza
authors.initials: A
authors.lastname: Gharabaghi
mesh_terms: D000328:Adult; D062207:Brain-Computer Interfaces; D002540:Cerebral Cortex / Q000502:physiology*; D004576:Electromyography / Q000379:methods; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009415:Nerve Net / Q000502:physiology; D058765:Neurofeedback / Q000379:methods*; D005082:Physical Exertion / Q000502:physiology; D011597:Psychomotor Performance / Q000502:physiology*; D012146:Rest / Q000502:physiology; D012371:Robotics / Q000379:methods*; D016526:Workload; D055815:Young Adult