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Related Experiment Video

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Bio-inspired EEG signal computing using machine learning and fuzzy theory for decision making in future-oriented

Haewon Byeon1, Aadam Quraishi2, Mohammed I Khalaf3

  • 1Department of AI and Software, Inje University, Gimhae 50834, Republic of Korea; Inje University Medical Big Data Research Center, Gimhae 50834, Republic of Korea.

SLAS Technology
|August 29, 2024
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Summary

This study introduces Fuzzy Brain-Control Fusion Control to improve brain-controlled vehicle performance. The new method enhances control accuracy by combining driver intent with automated decisions for better autonomous driving.

Keywords:
BCIBrain control vehiclesEEG signalsFuzzy theoryNeuron-electronics

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Area of Science:

  • Robotics and Artificial Intelligence
  • Neuroscience and Human-Computer Interaction

Background:

  • Brain-Controlled Vehicles (BCVs) rely on Brain-Computer Interfaces (BCIs) for driver input via electroencephalogram (EEG) signals.
  • Current BCIs face limitations in accuracy, command recognition, and execution speed, leading to suboptimal BCV control.
  • Improving BCV control performance while maintaining BCI accuracy is a significant challenge.

Purpose of the Study:

  • To introduce a novel fuzzy logic-based technique, Fuzzy Brain-Control Fusion Control, to enhance the control capabilities of BCVs.
  • To address the limitations of existing BCI systems in autonomous vehicle applications.
  • To develop a system that fuses driver intent with automated control for more robust and human-aligned vehicle operation.

Main Methods:

  • Utilized Fuzzy Discrete Event System (FDES) supervisory theory to validate the accuracy of driver's brain-controlled commands.
  • Developed a fuzzy logic-based automatic controller for real-time decision-making based on vehicle state.
  • Implemented a secondary fuzzy reasoning layer to fuse driver commands and automated decisions for final output.

Main Results:

  • Demonstrated the viability of the proposed Fuzzy Brain-Control Fusion Control technique using a Consistent State Visual Evoked Potential (SSVEP) BCI.
  • The fusion approach aims to create more accurate and human-intent-aligned adjustments in BCV operation.
  • The system shows potential for improving the control execution of BCI-powered vehicles.

Conclusions:

  • The Fuzzy Brain-Control Fusion Control method offers a promising approach to overcome current BCV performance limitations.
  • Further research is recommended to validate and optimize the system for enhanced control execution in BCI-fueled cars.
  • The technique holds potential for advancing autonomous driving technology through improved human-machine integration.