Brain-controlled unmanned aerial vehicle system based on meta brain computer interface open-source software platform_Journal of Biomedical Engineering

Authors：

LI Ang ^1,2 , MEI Jie ^1,2 , CHEN Weize ¹ , TAO Lingling ¹ ,  XU Minpeng ^1,3 , MING Dong ^1,3

1. Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China;
2. School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, P. R. China;
3. Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin 300392, P. R. China;

Corresponding?author：

XU Minpeng, Email: xmp52637@tju.edu.cn

Keywords：

Brain computer interface; Meta brain computer interface open-source software platform; Brain-controlled unmanned aerial vehicle

DOI：

10.7507/1001-5515.202506031

Video：

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Abstract Full text Figures/Tables Video References Cited by

Brain computer interface (BCI) system includes multiple links such as stimulus presentation, data acquisition, signal processing, external device control and command feedback. As an open-source software platform which covers all links of BCI chain, meta brain-computer interface (MetaBCI) has provided flexible solutions for effectively encoding, decoding and feeding back brain activities, but has not yet provided an integrated tool that can support the implementation of a complete BCI system. In view of the above shortcoming, this paper designed and constructed a brain-controlled unmanned aerial vehicle system by using MetaBCI, which realized the online control of the physical unmanned aerial vehicle. The results of the experiment involving 10 subjects indicated that the average online classification accuracy and information translate rate (ITR) of this system could reach 93.83% and 38.57 bits/min, respectively, which verified the feasibility of constructing a practical BCI system for external device control by using MetaBCI. Meanwhile, this paper elaborated the design idea, implementation process and the usage logic of MetaBCI toolkit involved in this brain-controlled unmanned aerial vehicle system in detail, hoping to provide guidance for subsequent developers to design and construct BCI systems that can meet individual needs by using MetaBCI independently.

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26.	Tai P, Ding P, Wang F, et al. Brain-computer interface paradigms and neural coding. Front Neurosci, 2024, 17: 1345961.
27.	Cui H, Chi X, Wang L, et al. A high-rate hybrid BCI system based on high-frequency SSVEP and sEMG. IEEE J Biomed Health Inform, 2023, 12(27): 5688-5698.
28.	Han J, Liu C, Chu J, et al. Effects of inter-stimulus intervals on concurrent P300 and SSVEP features for hybrid brain-computer interfaces. J Neurosci Methods, 2022, 372: 109535.
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34.	Chen X, Liu B, Wang Y, et al. A spectrally-dense encoding method for designing a high-speed SSVEP-BCI with 120 stimuli. IEEE Trans Neural Syst Rehabil Eng, 2022, 30: 2764-2772.
35.	Chen X, Wang Y, Nakanishi M, et al. High-speed spelling with a noninvasive brain-computer interface. Proc Natl Acad Sci U S A, 2015, 112(44): E6058-E6067.
36.	Nakanishi M, Wang Y, Chen X, et al. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis. IEEE Trans Biomed Eng, 2018, 65(1): 104-112.
37.	Chen X, Wang Y, Gao S, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain computer interface. J Neural Eng, 2015, 12(4): 046008.
38.	Dong Y, Zheng L, Pei W, et al. A 240-target VEP-based BCI system employing narrow-band random sequences. J Neural Eng, 2025, 22(2): 026024.
39.	Adin A, Krainski E T, Lenzi A, et al. Automatic cross-validation in structured models: is it time to leave out leave-one-out?. Spatial Stat, 2024, 62: 100843.

1. Barnova K, Mikolasova M, Kahankova R V, et al. Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction. Comput Biol Med, 2023, 163: 107135.
2. Kawala-Sterniuk A, Browarska N, Al-Bakri A, et al. Summary of over fifty years with brain-computer interfaces-a review. Brain Sci, 2021, 11(1): 43.
3. Edelman B J, Zhang S, Schalk G, et al. Non-invasive brain-computer interfaces: state of the art and trends. IEEE Rev Biomed Eng, 2025, 18: 26-49.
4. 邱爽, 楊幫華, 陳小剛, 等. 非侵入式腦-機接口編解碼技術研究進展. 中國圖象圖形學報, 2023, 28(6): 1543-1566.
5. Willett F R, Kunz E M, Fan C, et al. A high-performance speech neuroprosthesis. Nature, 2023, 620(7976): 1031-1036.
6. Zhao Z, Nie C, Jiang C, et al. Modulating brain activity with invasive brain-computer interface: a narrative review. Brain Sci, 2023, 13(1): 134.
7. Forenzo D, Zhu H, Shanahan J, et al. Continuous tracking using deep learning-based decoding for noninvasive brain-computer interface. PNAS Nexus, 2024, 4(3): 145.
8. Lin S, Jiang J, Huang K, et al. Advanced electrode technologies for noninvasive brain-computer interfaces. ACS Nano, 2023, 17(24): 24487-24513.
9. Ramadan R A, Altamimi A B. Unraveling the potential of brain-computer interface technology in medical diagnostics and rehabilitation: a comprehensive literature review. Health Technol, 2024, 14: 263-276.
10. Wang F, Wen Y, Bi J, et al. A portable SSVEP-BCI system for rehabilitation exoskeleton in augmented reality environment. Biomed Signal Process Control, 2023, 83: 104664.
11. Tsai P C, Akpan A, Tang K T, et al. Brain computer interfaces for cognitive enhancement in older people-challenges and applications: a systematic review. BMC Geriatr, 2025, 25(1): 36.
12. 朱藝森, 季洲宇, 李舒然, 等. 面向智慧醫療的便攜式穩態視覺誘發電位腦機接口系統. 生物醫學工程學雜志, 2025, 42(3): 455-463.
13. Li J, Wang L, Zhang Z, et al. Analysis and recognition of a novel experimental paradigm for musical emotion brain-computer interface. Brain Res, 2024, 1839: 149039.
14. Houssein E H, Hammad A, Ali A A. Human emotion recognition from EEG-based brain-computer interface using machine learning: a comprehensive review. Neural Comput Appl, 2022(34): 12527-12557.
15. Hussain S A H, Raza I, Hussain S A, et al. A mental state aware brain computer interface for adaptive control of electric powered wheelchair. Sci Rep, 2025, 15(1): 9880.
16. Xu B, Li W, Liu D, et al. Continuous hybrid BCI control for robotic arm using noninvasive electroencephalogram, computer vision, and eye tracking. Mathematics, 2022, 10(4): 618.
17. Mai X, Ai J, Ji M, et al. A hybrid BCI combining SSVEP and EOG and its application for continuous wheelchair control. Biomed Signal Process Control, 2024, 88: 105530.
18. Xie S, Gao W, Zeng Z, et al. Multi-degree-of-freedom unmanned aerial vehicle control combining a hybrid brain-computer interface and visual obstacle avoidance. Eng Appl Artif Intell, 2024, 133: 108294.
19. Liu Z, Mei J, Tang J, et al. A memristor-based adaptive neuromorphic decoder for brain-computer interfaces. Nat Electron, 2025, 8: 362-372.
20. Zabcikova M, Koudelkova Z, Jasek R, et al. Recent advances and current trends in brain-computer interface research and their applications. Int J of Dev Neurosci, 2022, 82(2): 107-123.
21. Hu H, Wang Z, Zhao X, et al. A Survey on brain-computer interface-inspired communications: opportunities and challenges. IEEE Commun Surv Tutorials, 2025, 27(1): 108-139.
22. Lyu X, Ding P, Li S, et al. Human factors engineering of BCI: an evaluation for satisfaction of BCI based on motor imagery. Cogn Neurodyn, 2023, 17(1): 105-118.
23. 曹洪濤, 鐘子平, 陳遠方, 等. 非侵入式腦機接口控制策略的研究進展. 生物醫學工程學雜志, 2022, 39(5): 1033-1040.
24. Mei J, Luo R, Xu L, et al. MetaBCI: an open-source platform for brain-computer interfaces. Comput Biol Med, 2024, 168: 107806.
25. 何峰, 王昶皓, 王坤, 等. 通用腦-機接口軟件平臺發展現狀. 中國生物醫學工程學報, 2024, 43(5): 610-619.
26. Tai P, Ding P, Wang F, et al. Brain-computer interface paradigms and neural coding. Front Neurosci, 2024, 17: 1345961.
27. Cui H, Chi X, Wang L, et al. A high-rate hybrid BCI system based on high-frequency SSVEP and sEMG. IEEE J Biomed Health Inform, 2023, 12(27): 5688-5698.
28. Han J, Liu C, Chu J, et al. Effects of inter-stimulus intervals on concurrent P300 and SSVEP features for hybrid brain-computer interfaces. J Neurosci Methods, 2022, 372: 109535.
29. Liu S, Ming Z, Liu M, et al. Remote-oriented brain-controlled unmanned aerial vehicle for IoT. IEEE Internet Things J, 2024, 11(17): 29202-29214.
30. Niu L, Bin J, Wang J, et al. A dynamically optimized time window length for SSVEP based hybrid BCI-VR system. Biomed Signal Process Control, 2023, 84: 104826.
31. Tao L, Cao T, Wang Q, et al. Application of self-adaptive multiple-kernel extreme learning machine to improve MI-BCI performance of subjects with BCI illiteracy. Biomed Signal Process Control, 2023, 79(2): 104183.
32. Hu L, Gao W, Lu Z, et al. Subject-independent wearable P300 brain-computer interface based on convolutional neural network and metric learning. IEEE Trans Neural Syst Rehabil Eng, 2024, 32: 3543-3553.
33. Cao B, Tsai C L-T, Zhou N, et al. A novel 3D paradigm for target expansion of augmented reality SSVEP. IEEE Trans Neural Syst Rehabil Eng, 2025, 33: 1562-1573.
34. Chen X, Liu B, Wang Y, et al. A spectrally-dense encoding method for designing a high-speed SSVEP-BCI with 120 stimuli. IEEE Trans Neural Syst Rehabil Eng, 2022, 30: 2764-2772.
35. Chen X, Wang Y, Nakanishi M, et al. High-speed spelling with a noninvasive brain-computer interface. Proc Natl Acad Sci U S A, 2015, 112(44): E6058-E6067.
36. Nakanishi M, Wang Y, Chen X, et al. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis. IEEE Trans Biomed Eng, 2018, 65(1): 104-112.
37. Chen X, Wang Y, Gao S, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain computer interface. J Neural Eng, 2015, 12(4): 046008.
38. Dong Y, Zheng L, Pei W, et al. A 240-target VEP-based BCI system employing narrow-band random sequences. J Neural Eng, 2025, 22(2): 026024.
39. Adin A, Krainski E T, Lenzi A, et al. Automatic cross-validation in structured models: is it time to leave out leave-one-out?. Spatial Stat, 2024, 62: 100843.

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