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Comparative analysis of MLP and KAN neural network architectures in neurointerface technologies

НазваComparative analysis of MLP and KAN neural network architectures in neurointerface technologies
Назва англійськоюComparative analysis of MLP and KAN neural network architectures in neurointerface technologies
АвториYuriy Petrov, Oleh Pastukh
ПринадлежністьTernopil Ivan Puluj National Technical University, Ternopil, Ukraine
Бібліографічний описComparative analysis of MLP and KAN neural network architectures in neurointerface technologies / Yuriy Petrov, Oleh Pastukh // Scientific Journal of TNTU. — Tern.: TNTU, 2025. — Vol 119. — No 3. — P. 107–114.
Bibliographic description:Petrov Y., Pastukh O. (2025) Comparative analysis of MLP and KAN neural network architectures in neurointerface technologies. Scientific Journal of TNTU (Tern.), vol 119, no 3, pp. 107–114.
DOI: https://doi.org/10.33108/visnyk_tntu2025.03.107
УДК

681.3

Ключові слова

neural network, neurointerface, MLP, KAN, brain-computer interface, artificial intelligence, machine learning. 

This article explores the relevance of neurointerface technologies, particularly for assisting individuals with disabilities through advanced prosthetics. It examines the use of two neural network architectures, MLP (multilayer perceptron) and KAN (Kolmogorov Arnold network), for classifying finger movements based on brain signals. Results indicate that KAN models show an advantage in accuracy with smaller datasets and a more compact model size, though they require more computational resources and longer training times. In contrast, MLP is faster to train and slightly more effective on larger datasets, highlighting the potential for further development in neurointerface-based prosthetic solutions.

 

ISSN:2522-4433
Перелік літератури
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References:
  1. Evan D., Nick N., Josh W. (2024). The Regents of the University of Michigan. Mind control prothesis. Available at: https://spotlight.engin.umich.edu/mind-control-prosthesis/ (accessed 05.10.2024).
  2. Nishal P., Matthew S., Nick H., Foram K., (2024). A flexible intracortical brain-computer interface for typing using finger movements. Cord Spring Harbor laboratory, Available at: https://doi.org/10.1101/ 2024.04.22.5906301 (accessed 05.10.2024).
  3. Musk E., Neuralink (2019) An Integrated Brain-Machine Interface Platform With Thousands of Channels. Journal of medical Internet research, vol. 21, no. 10.  Available at: https://pmc.ncbi.nlm.nih.gov/articles/ PMC6914248/ (accessed: 06.10.2024).
  4. Willett F. R., Avansino D. T., Hochberg L. R. et al. (2021) High-performance brain-to-text communication via handwriting. Nature 593, 249–254. Available at: https://doi.org/10.1038/s41586-021-03506-2 (accessed: 06.10.2024).
  5. Pastukh O., Yatsyshyn V., (2023) Brain-Computer Interaction Neurointerface Based On Artificial Intelligence And Its Parallel Programming Using High-Performance Calculation On Cluster Mobile Devices. Scientific Journal of the Ternopil National Technical University, no. 4. Availabe at: https://doi. org/10.33108/visnyk_tntu2023.04.026 (accessed: 07.10.2024).
  6. Levi T, Bonifazi P, Massobrio P and Chiappalone M., (2018) Editorial: Closed-Loop Systems for Next-Generation Neuroprostheses. Front. Neurosci. 12:26. Available at: https://www.frontiersin.org/journals/ neuroscience/articles/10.3389/fnins.2018.00026/full (accessed 07.10.2024).
  7. Agrawal A., Ray B., & Bal R. Redolfi Riva E., Micera S. (2021). Progress and challenges of implantable neural interfaces based on nature-derived materials. 2018. Bioelectron Med 7, 6. Available at: https://doi. org/10.1186/s42234-021-00067-7 (accessed 08.10.2024).
  8. Marco V., Leigh R. Applications of neural interfaces in prosthetic control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26 (1), 101–110. Available at: https://doi.org/10.1109/TNSRE. 2018.2792051 (accessed: 08.10.1024).
  9. Joon Y. L, Ssang H. S. (2024) EEG-Based Emotion Recognition Using Deep Learning Model for Workers Safety. Nanotechnology Perceptions, vol. 20, no. S2. Available at: https://doi.org/10.62441/nano-ntp.v20 iS2.25 (accessed: 08.10.2024).
  10. Apicella A., Arpaia P., Giugliano S., Mastrati G., Moccaldi N. (2022) High-wearable EEG-based transducer for engagement detection in pediatric rehabilitation Brain-Computer Interfaces, 9 (3), pp. 129–139.
  11. Memmott T., Kocanaogullari A., Lawhead M., Fried-Oken M., Oken B. (2021) BciPy: brain-computer interface software in Python. Brain-Computer Interfaces, 8 (4), pp. 137–153.
  12. McFarland D. J., Norman S. L., Sarnacki W. A., Reinkensmeyer D. J., Wolpaw J. R. (2020) BCI-based sensorimotor rhythm training can affect individuated finger movements. Brain-Computer Interfaces 7 (1–2), pp. 38–46.
  13. Chen C., Chen P., Belkacem A. N., Wang C., Ming D. 2020. Neural activities classification of left and right finger gestures during motor execution and motor imagery. Brain-Computer Interfaces pp. 1–11.
  14. Cross-Modulated Amplitudes and Frequencies of Epileptic EEG. Available at: https://www.researchgate. net/publication/335581366_Cross Modulated_Amplitudes_and_Frequencies_of_Epileptic_EEG (accessed: 0.7.10.2024).
  15. Ziming L., Yixuan W., Sachin V., Fabian R., James H., Marin S., Thomas Y. H., Max T. 2024. KAN: Kolmogorov–Arnold Networks. Available at: doi.org/10.48550/arXiv.2404.19756 (accessed: - 25.10.2024).
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  17. Feed forward network diagram library. Available at: https://github.com/martisak/dotnets (accessed: 22.10.2024).
  18. Pykan library documentation. Available at: https://kindxiaoming.github.io/pykan/modules.html (accessed: 25.10.2024).
  19. Sklearn python machine learning library. MLPClassifier documentation. Available at: https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html (accessed: 21.10.2024 )
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