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Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation.

C Vidaurre1, A Ramos Murguialday2, S Haufe3

  • 1Statistics, Informatics and Mathematics Dp, Public University of Navarre, Pamplona, Spain; Machine Learning Group, EE & Computer Science Faculty, TU-Berlin, Germany.

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|June 4, 2019
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Summary

Assistive muscular stimulation during motor imagery training improves brain-computer interface (BCI) accuracy. This novel approach enhances BCI control for users with limited accuracy, offering new training strategies.

Keywords:
Afferent patternsBrain-computer interfacing (BCI) inefficiencyEfferent patternsMotor imagery (MI)Sensory threshold neuromuscular electrical stimulation (STM)

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Brain-computer interface (BCI) control accuracy is crucial for user efficacy.
  • Existing BCI methods face challenges with users exhibiting insufficient control accuracy.
  • Procedural strategies are needed to enhance BCI performance for specific user groups.

Purpose of the Study:

  • To investigate if assistive muscular stimulation below the motor threshold can improve motor imagery classification for BCI.
  • To evaluate the effectiveness of training classifiers on data combining motor imagery and sub-threshold stimulation (BOTH) versus motor imagery alone (MI).
  • To explore potential applications for individuals with motor impairments, such as ALS and stroke patients.

Main Methods:

  • Offline analysis and online experiments were conducted with healthy participants.
  • Experimental conditions included: Motor Imagery (MI) alone, sensory threshold stimulation (STM) alone, and BOTH (MI with STM).
  • Classifiers were trained on MI or BOTH data, and neurofeedback was provided during online MI tasks.

Main Results:

  • Offline analysis demonstrated superior BCI accuracy when decoding MI using a classifier trained on BOTH data compared to MI data alone.
  • Online experiments confirmed improved accuracy for MI decoding when using classifiers trained on BOTH data.
  • Sensorimotor connectivity patterns in specific frequency bands during the BOTH condition predicted performance in the MI condition.

Conclusions:

  • Training BCI classifiers with data from combined motor imagery and sub-threshold neuromuscular electrical stimulation (BOTH) enhances BCI accuracy.
  • This approach offers a promising new avenue for training sensorimotor rhythm-based BCIs, especially for users with control difficulties.
  • It presents a viable alternative for patients with motor impairments (e.g., ALS, stroke) who retain afferent pathway function.