Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)

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Research on Brain-Computer Interface-Based Unmanned Aerial Vehicle Control System

Authors
Jinghua Wang1, *
1Chang’an Dublin International College of Transportation at Chang’an University, Chang’an University, Xi’an, 710000, China
*Corresponding author. Email: 2023906043@chd.edu.cn
Corresponding Author
Jinghua Wang
Available Online 31 August 2025.
DOI
10.2991/978-94-6463-821-9_85How to use a DOI?
Keywords
Unmanned aerial vehicle; brain-computer interface; electroencephalography; motor imagery
Abstract

Unmanned Aerial Vehicles (UAVs) are extensively utilized in both civilian and military sectors; yet, conventional UAV control algorithms encounter efficiency limitations in complicated situations. This research proposes a BCI-based UAV control system to enhance UAV control efficiency. This design system basically comprises a brain signal acquisition and processing module, a BCI control module, and a UAV flight control module. This design involves the acquisition of non-invasive electroencephalogram (EEG) signals, artifact removal via the Independent Component Analysis (ICA) algorithm, denoising through the Butterworth band-pass filter, and signal normalization using the Z-Score algorithm. The system operates on the Motion Imagery (MI) paradigm. The Fast Fourier Transform (FFT) algorithm is employed to extract signal features, while the Linear Discriminant Analysis (LDA) algorithm is utilized for classification. The training results indicate that the cross-validation accuracy of the LDA classifier in the test is 62.5%, demonstrating the design’s efficacy and viability. The signal processing algorithm of BCI can be further refined in the future, facilitating the extensive implementation of this technology in the UAV sector.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
Series
Advances in Engineering Research
Publication Date
31 August 2025
ISBN
978-94-6463-821-9
ISSN
2352-5401
DOI
10.2991/978-94-6463-821-9_85How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Jinghua Wang
PY  - 2025
DA  - 2025/08/31
TI  - Research on Brain-Computer Interface-Based Unmanned Aerial Vehicle Control System
BT  - Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
PB  - Atlantis Press
SP  - 888
EP  - 898
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-821-9_85
DO  - 10.2991/978-94-6463-821-9_85
ID  - Wang2025
ER  -