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Related Experiment Video

Updated: Sep 1, 2025

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
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Driving Mode Selection through SSVEP-Based BCI and Energy Consumption Analysis.

Juai Wu1, Zhenyu Wang2, Tianheng Xu2

  • 1College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Sensors (Basel, Switzerland)
|August 12, 2022
PubMed
Summary

This study introduces a brain-controlled driving mode selection system using steady-state visual-evoked potentials (SSVEP). The novel system achieves high accuracy, offering a new approach for brain-computer interface applications in vehicles.

Keywords:
brain-controlled driving mode selectionbrain–computer interface (BCI)steady-state visual-evoked potential (SSVEP)

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Area of Science:

  • Neuroscience
  • Human-Computer Interaction
  • Automotive Engineering

Background:

  • Brain-computer interfaces (BCI) are gaining attention across disciplines.
  • BCI applications in the automotive industry, particularly for driving mode selection, remain underexplored.
  • Driving modes vary based on road conditions and driver preferences.

Purpose of the Study:

  • To propose a novel brain-controlled driving mode selection system.
  • To investigate the effectiveness of steady-state visual-evoked potentials (SSVEP) for this application.
  • To introduce a new algorithm for enhancing SSVEP detection.

Main Methods:

  • Development of an SSVEP-based system for driving mode selection via visual gaze.
  • Introduction of the inter-trial distance minimization analysis (ITDMA) algorithm for improved SSVEP detection.
  • Validation through both offline and real-time experiments.

Main Results:

  • High selection accuracy up to 92.3% was achieved.
  • Accuracy is influenced by flickering duration, EEG channel count, and training signal quantity.
  • Energy consumption differences between the proposed and traditional systems were investigated.

Conclusions:

  • The proposed SSVEP-based BCI system enables accurate driving mode selection.
  • Detection errors are a key factor in the energy consumption differences observed.
  • This research opens avenues for BCI integration in vehicle control systems.