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Human-machine interface for two-dimensional steering control with the auricular muscles.

Daniel J L L Pinheiro1,2, Jean Faber1,3, Silvestro Micera2,4

  • 1Division of Neuroscience, Department of Neurology and Neurosurgery, Neuroengineering and Neurocognition Laboratory, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil.

Frontiers in Neurorobotics
|June 21, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new way for people to control devices using their auricular muscles (AM). Naïve participants learned to move a cursor with two degrees of freedom, showing improved performance and low cognitive load, paving the way for new brain-computer interfaces.

Keywords:
Neuroprostheticsauricular musclehuman-machine interfacemotor decodingsteering control

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Rehabilitation Technology

Background:

  • Human-machine interfaces (HMIs) offer potential for individuals with motor disabilities, such as spinal cord injury, to regain control over external devices.
  • Existing HMI solutions require improvements in decoding algorithms, hardware, and user learning.
  • Auricular muscles (AMs) are vestigial muscles often preserved after neurological damage, making them a promising target for HMIs.

Purpose of the Study:

  • To develop and evaluate a novel decoding and training paradigm for HMIs using auricular muscles.
  • To enable naïve participants to control a two-dimensional virtual cursor with two degrees of freedom using their AMs.
  • To assess the learning curve, performance, and cognitive load associated with this AM-based HMI.

Main Methods:

  • Surface electromyographic (sEMG) signals from AMs were used to modulate cursor velocity and direction in a 2D space.
  • A locking mechanism was implemented to allow independent control and stopping of cursor movement along each axis.
  • A five-session training protocol with a center-out task was administered to five non-disabled volunteers, including a dual-task condition to assess cognitive load.

Main Results:

  • Participants demonstrated significant improvement in task success rate (from 52.78% to 72.22%) and trajectory performance after training.
  • The AM-based HMI was effective even under cognitively demanding dual-task conditions (66.67% success rate).
  • Participants reported reduced mental demand and effort in later training sessions, as measured by the Nasa Task Load Index.

Conclusions:

  • Naïve individuals can learn to effectively control a two-degrees-of-freedom cursor using their auricular muscles with minimal cognitive burden.
  • This novel AM-based decoding and training paradigm represents a promising first step towards developing advanced HMIs for individuals with motor impairments.
  • The preserved nature of AMs post-neurological injury highlights their potential for restoring functional control in assistive technologies.