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Human-machine interfaces based on EMG and EEG applied to robotic systems.

Andre Ferreira1, Wanderley C Celeste, Fernando A Cheein

  • 1Department of Electrical Engineering, Federal University of Espirito Santo, Av, Fernando Ferrari, 514, 29075-910, Vitoria-ES, Brazil. andrefer@ele.ufes.br

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Low-cost electro-biological Human-Machine Interfaces (HMIs) using electromyography (EMG) and electroencephalography (EEG) show promise for controlling robotic systems. These systems offer efficient control for individuals with neuromotor diseases.

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Area of Science:

  • Biomedical Engineering
  • Robotics
  • Neuroscience

Background:

  • Developed two electro-biological Human-Machine Interfaces (HMIs) utilizing electromyography (EMG) and electroencephalography (EEG) signals.
  • Both HMIs are designed as low-cost solutions with simple data acquisition and processing systems.
  • Interfaces were applied to robotic systems, including mobile robots and a robotic manipulator.

Purpose of the Study:

  • To analyze the performance of EMG-based and EEG-based HMIs in controlling robotic systems.
  • To evaluate the feasibility of these HMIs for assisting individuals with neuromotor diseases.
  • To assess the efficiency and learning curve associated with both HMI types.

Main Methods:

  • EMG-based HMI tested on a mobile robot with eight participants performing eye blinks for control.
  • EEG-based HMI tested on a mobile robot and robotic manipulator with 25 participants, including those with neurological conditions.
  • Performance metrics included eye blink detection accuracy and user training time.

Main Results:

  • The EMG-based HMI achieved an average eye blink detection accuracy of approximately 95%.
  • All 25 participants successfully used the EEG-based HMI after a single training session, with most learning in under 15 minutes.
  • Training times for the EEG-based HMI ranged from 3 to 50 minutes.

Conclusions:

  • The developed HMIs demonstrate potential for commanding devices and assisting individuals with neuromotor diseases.
  • Future work includes integrating the EEG-based HMI into an autonomous wheelchair for enhanced mobility assistance.
  • Further research aims to improve the robustness and speed of the EEG-based HMI for individuals with severe motor dysfunctions.