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Hybrid BCI for Meal-Assist Robot Using Dry-Type EEG and Pupillary Light Reflex.

Jihyeon Ha1, Sangin Park2, Yaeeun Han1,3

  • 1Bionics Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea.

Biomimetics (Basel, Switzerland)
|February 25, 2025
PubMed
Summary

This study introduces a practical hybrid brain-computer interface (BCI) using dry EEG and pupillary light reflex (PLR) for a meal-assist robot. The system achieves high accuracy, enhancing independence for individuals with disabilities.

Keywords:
brain–computer interface (BCI)dry-type EEGelectroencephalography (EEG)electromyogram (EMG)eyewear-type infrared camerasflash visual evoked potential (FVEP)meal-assist robotpupillary light reflex (PLR)

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Rehabilitation Technology

Background:

  • Existing wet EEG-based brain-computer interfaces (BCIs) offer high accuracy but lack practical usability.
  • There is a need for user-friendly assistive technologies that enhance independence for elderly and disabled individuals.

Purpose of the Study:

  • To develop and evaluate a hybrid BCI system combining dry-type EEG-based flash visual-evoked potentials (FVEP) and pupillary light reflex (PLR).
  • To control an LED-based meal-assist robot, addressing practical challenges of current BCI systems.

Main Methods:

  • Integration of dry-type EEG with eyewear-type infrared cameras for FVEP and PLR signal acquisition.
  • Development of a hybrid BCI system for controlling a meal-assist robot.
  • Offline experiments for classification accuracy and information transfer rate (ITR) assessment.

Main Results:

  • Achieved an average accuracy of 88.59% for four target classifications in offline experiments.
  • Demonstrated an information transfer rate (ITR) of 18.23 bit/min.
  • Successful real-time implementation using PLR for meal cycle initiation and EMG for chewing detection.

Conclusions:

  • The hybrid FVEP-PLR BCI system offers a practical and high-performance solution for assistive technology.
  • This approach enhances user autonomy and dignity in daily activities through intuitive robot control.
  • The study advances BCI applications in real-world settings, particularly for meal assistance.