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Optimization of SSVEP-BCI Virtual Reality Stereo Stimulation Parameters Based on Knowledge Graph.

Shixuan Zhu^1,2, Jingcheng Yang^1,2, Peng Ding^1,2

¹School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650032, China.

|May 27, 2023

Summary

This study optimized virtual reality (VR) stereoscopic stimulation targets for steady-state visually evoked potential brain-computer interfaces (SSVEP-BCI). A knowledge graph identified optimal parameters like sphere shape, blue color, and 13Hz frequency for enhanced BCI performance.

Keywords:

BCI SSVEP VR stereoscopic stimulation knowledge graph parameter optimization

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Area of Science:

Neuroscience
Human-Computer Interaction
Virtual Reality

Background:

Steady-state visually evoked potential (SSVEP) is a key brain-computer interface (BCI) technology with VR applications.
Current SSVEP-BCI research predominantly uses 2D plane stimulation targets (PSTs), with limited exploration of 3D stereo stimulation targets (SSTs).

Purpose of the Study:

To optimize virtual reality stereoscopic stimulation parameters for SSVEP-BCI.
To develop a parameter knowledge graph for intuitive selection of optimal SSVEP-BCI stimulus parameters.

Main Methods:

An online VR stereoscopic stimulation SSVEP-BCI system was developed.
A parameter dictionary for VR stereoscopic stimulation (shape, color, frequency) was established.
Experimental data from 10 subjects were collected and analyzed to construct a knowledge graph.

Main Results:

Optimal classification performances were achieved with sphere shape (91.85%), blue color (94.26%), and 13Hz frequency (95.93%).
SSVEP-BCI performance significantly varies with different combinations of VR stereo stimulation parameters.
The developed knowledge graph effectively aids in selecting appropriate SST parameters.

Conclusions:

The study successfully optimized VR SST parameters for SSVEP-BCI using a knowledge graph.
The findings provide a framework for enhancing SSVEP-BCI applications in VR environments.
The parameter knowledge graph is a valuable tool for advancing SSVEP-BCI and VR integration.