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Brain-Machine Neurofeedback: Robotics or Electrical Stimulation?

Robert Guggenberger1, Monika Heringhaus1, Alireza Gharabaghi1

  • 1Institute for Neuromodulation and Neurotechnology, Department of Neurosurgery and Neurotechnology, University of Tübingen, Tübingen, Germany.

Frontiers in Bioengineering and Biotechnology
|August 1, 2020
PubMed
Summary

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Brain-machine interfaces (BMI) using robotic or functional electrical stimulation (FES) feedback show similar workloads, with mental demand being highest for both. Robotic feedback resulted in better performance than FES, informing neurorehabilitation interface design.

Area of Science:

  • Neuroscience
  • Rehabilitation Engineering
  • Human-Computer Interaction

Background:

  • Brain-machine interfaces (BMI) are explored for neurorehabilitation, aiding individuals with paralysis by providing movement assistance.
  • Robotic orthoses and functional electrical stimulation (FES) offer movement feedback, closing the sensorimotor loop, but their comparative user workload is unknown.
  • Controlling neurorehabilitation devices can be challenging, necessitating research into user experience and workload demands.

Purpose of the Study:

  • To directly compare the user workload of robotic orthosis feedback versus functional electrical stimulation (FES) in a brain-machine interface (BMI) context.
  • To evaluate performance differences between robotic and FES feedback modalities during BMI control.
  • To inform the design of human-centered neurorehabilitation interfaces for improved user interaction and acceptance.
Keywords:
brain-computer interfacebrain-robot interfaceclosed-loop stimulationneuromuscular electrical stimulationrobotic rehabilitationstate-dependent stimulation

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Main Methods:

  • Twenty healthy subjects controlled a BMI using kinesthetic motor imagery of finger extension.
  • EEG beta-band desynchronization was translated into passive hand opening via robotic orthosis or FES in a randomized, cross-over design.
  • The NASA Task Load Index (NASA-TLX) questionnaire assessed mental demand, physical demand, temporal demand, performance, effort, and frustration.

Main Results:

  • Robotic and FES feedback exhibited similar overall workloads when components were weighted and rated.
  • Mental demand was the most significant workload component for both feedback types, exceeding that of active movement with EMG feedback.
  • The FES task resulted in significantly higher physical demand (p=0.0368) and lower temporal demand (p=0.0403) than the robotic task.
  • Significantly more movements were initiated with robotic feedback (17.22) compared to FES (16.46) (p=0.016), despite comparable BMI classification accuracy.

Conclusions:

  • Both robotic and FES feedback modalities present comparable workloads, with mental demand being a key factor.
  • Robotic feedback appears to offer superior performance in terms of movement initiation compared to FES.
  • Findings suggest that optimizing neurorehabilitation interfaces for bidirectional interaction is crucial for user acceptance and effective therapy.