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

Haewon Byeon ¹, Aadam Quraishi ², Mohammed I Khalaf ³

¹Department of AI and Software, Inje University, Gimhae 50834, Republic of Korea; Inje University Medical Big Data Research Center, Gimhae 50834, Republic of Korea.
²M.D Research, Intervention Treatment Institute, Houston, TX, USA.
³Department of Computer Science, Al Maarif University College, Al Anbar, 31001, Iraq.
⁴Department of Electronics and Communication Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, India.
⁵Computer Science Department, Jamia Hamdard Delhi, New Delhi, India.
⁶Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh 13713, Kingdom of Saudi Arabia.
⁷Department of CSE, Acharya Nagarjuna University, Nagarjuna Nagar, Guntur, 522510, India.
⁸Independent Research, University of Texas, Austin, USA.
⁹Department of Electrical and Electronics Engineering, College of Engineering and Computer Science, Jazan University, Jazan, Kingdom of Saudi Arabia.
¹⁰Model Institute of Engineering and Technology, Jammu, J&K, India.

⁰Department of AI and Software, Inje University, Gimhae 50834, Republic of Korea; Inje University Medical Big Data Research Center, Gimhae 50834, Republic of Korea.

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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.

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.

Keywords:

BCI Brain control vehicles EEG signals Fuzzy theory Neuron-electronics