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Updated: Aug 8, 2025

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients
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Brain-Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for

Nannaphat Siribunyaphat1, Yunyong Punsawad1,2

  • 1School of Informatics, Walailak University, Nakhon Si Thammarat 80160, Thailand.

Sensors (Basel, Switzerland)
|February 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel brain-computer interface (BCI) using electroencephalography (EEG) and quick-response (QR) codes for wheelchair control. The system achieved 92% accuracy but requires further enhancement to address visual fatigue and control speed.

Keywords:
QR codebrain–computer interfacequick responsesteady-state visual evoked potentialwheelchair

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Brain-computer interfaces (BCIs) offer control solutions for individuals with severe physical disabilities.
  • Existing electroencephalogram (EEG)-based BCIs utilize steady-state visually evoked potentials (SSVEPs) for device operation.
  • Developing practical brain-controlled wheelchairs remains a significant research objective.

Purpose of the Study:

  • To develop and evaluate a robust EEG-based BCI system for wheelchair control.
  • To investigate the efficacy of a quick-response (QR) code visual stimulus pattern for SSVEP generation.
  • To compare relative power spectrum density (PSD) with absolute PSD for SSVEP feature extraction.

Main Methods:

  • Utilized a novel QR code visual stimulus pattern with four flickering frequencies to generate four distinct commands.
  • Employed a relative power spectrum density (PSD) method for SSVEP feature extraction, comparing its performance against an absolute PSD method.
  • Conducted experiments to assess the real-time processing accuracy and efficiency of the proposed BCI system for wheelchair control.

Main Results:

  • The proposed SSVEP method and algorithm demonstrated an average real-time classification accuracy of approximately 92%.
  • The BCI system achieved functional wheelchair control in a simulated environment.
  • Compared to keyboard control, the proposed BCI control for the simulated wheelchair was approximately five times slower.

Conclusions:

  • The developed SSVEP method using a QR code pattern is a viable approach for BCI-based wheelchair control.
  • The system shows promise for assisting individuals with severe physical disabilities.
  • Further research is needed to mitigate visual fatigue associated with prolonged use and enhance overall control efficiency.