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Updated: Aug 8, 2025

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

Published on: April 18, 2025

Brain-Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for

Nannaphat Siribunyaphat¹, Yunyong Punsawad^1,2

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

Sensors (Basel, Switzerland)

|February 28, 2023

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 code brain–computer interface quick response steady-state visual evoked potential wheelchair

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Published on: April 12, 2016

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.