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A Novel Asynchronous Brain Signals-Based Driver-Vehicle Interface for Brain-Controlled Vehicles.

Jinling Lian¹, Yanli Guo², Xin Qiao¹

¹Beijing Institute of Basic Medical Sciences, 27 Taiping Rd., Beijing 100850, China.

Bioengineering (Basel, Switzerland)

|September 28, 2023

Summary

This study introduces a novel electroencephalogram (EEG) signal-based driver-vehicle interface (DVI) enabling continuous control of vehicles. This brain-controlled vehicle technology offers a potential solution for individuals with neuromuscular disorders to regain driving independence.

Keywords:

brain signals brain-controlled vehicles command decoding algorithm driver–vehicle interfaces

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Area of Science:

Neuroscience and Human-Computer Interaction
Intelligent Transportation Systems
Biomedical Engineering

Background:

Neuromuscular disorders can impair driving ability.
Brain-controlled vehicle interfaces offer a potential solution for restoring mobility.
Existing interfaces often lack continuous and asynchronous control capabilities.

Purpose of the Study:

To develop a novel electroencephalogram (EEG) signal-based driver-vehicle interface (DVI).
To enable continuous and asynchronous control of brain-controlled vehicles.
To assess the feasibility and accuracy of the developed DVI for vehicle operation.

Main Methods:

Development of a DVI comprising a user interface, command decoding algorithm, and control model.
User interface designed to present commands and elicit specific brain patterns.
Offline and real-time experiments using a simulated vehicle on a U-turn road.

Main Results:

Offline testing achieved 83.59% accuracy for motion control commands and 90.06% accuracy in idle state recognition.
Real-time experiments demonstrated the feasibility of continuous and asynchronous vehicle control.
The DVI successfully translated EEG signals into vehicle control actions.

Conclusions:

The developed EEG-based DVI is feasible for continuous and asynchronous control of vehicles.
This technology advances brain-controlled vehicle research and driver-vehicle interfaces.
Provides valuable insights for multimodal interaction and intelligent vehicle development.