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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Updated: May 5, 2026

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
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[Visual object detection system based on augmented reality and steady-state visual evoked potential].

Meng'ao Guo1, Banghua Yang1, Yiting Geng1

  • 1School of Mechanical and Electrical Engineering and Automation, Shanghai University, Shanghai 200444, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|September 1, 2024
PubMed
Summary

This study introduces an augmented reality brain-computer interface (AR-BCI) using steady-state visual evoked potentials (SSVEP) for object selection. The system achieved 90.6% accuracy, aiding individuals with disabilities in real-world tasks.

Keywords:
Augmented realityBrain-computer interfaceSteady-state visual evoked potentialTarget recognition

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Area of Science:

  • Neuroscience
  • Human-Computer Interaction
  • Computer Vision

Context:

  • Brain-computer interfaces (BCI) offer potential for assistive technology.
  • Augmented reality (AR) enhances real-world interactions.
  • Steady-state visual evoked potentials (SSVEP) are a common BCI signal.

Purpose:

  • To develop and evaluate an AR-BCI system for real-world object selection.
  • To integrate object detection and AR for visual stimuli generation.
  • To utilize SSVEP for interpreting user gaze and identifying selected objects.

Summary:

  • An AR-BCI system was created, overlaying visual enhancements on real-world objects.
  • Object detection and AR technology generated stimuli to elicit SSVEP signals.
  • An adaptive filter bank canonical correlation analysis processed SSVEP signals for object identification.
  • The system achieved an average accuracy of 90.6% in identifying visually targeted objects.

Impact:

  • Demonstrates the feasibility of SSVEP-based BCI in real-life object selection.
  • Provides a novel assistive tool for individuals with mobility impairments.
  • Expands BCI applications beyond laboratory settings into practical scenarios.