Comparative analysis of MLP and KAN neural network architectures in neurointerface technologies
Article Sidebar
Main Article Content
Abstract
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.
Article Details
Issue
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
1. Evan. D., Nick. N., Josh W., The Regents of the University of Michigan. Mind control prothesis. 2024. Available at: https://spotlight.engin.umich.edu/mind-control-prosthesis/ (accessed 05.10.2024).
2. Nishal P., Matthew S., Nick H., Foram K., A flexible intracortical brain-computer interface for typing using finger movements. Cord Spring Harbor laboratory, 2024. Available at: https://doi.org/10.1101/2024.04.22.5906301 (accessed 05.10.2024).
3. Musk E., Neuralink, An Integrated Brain-Machine Interface Platform With Thousands of Channels. Journal of medical Internet research, 2019, 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. High-performance brain-to-text communication via handwriting. Nature 593, 249–254 (2021). Available at: https://doi.org/10.1038/s41586-021-03506-2 (accessed 06.10.2024).
5. Pastukh O., Yatsyshyn V., 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 2023, № 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. Progress and challenges of implantable neural interfaces based on nature-derived materials. 2018. Bioelectron Med 7, 6 (2021). 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. EEG-Based Emotion Recognition Using Deep Learning Model for Workers Safety. Nanotechnology Perceptions, 2024, Vol. 20, no. S2. Available at: https://doi.org/10.62441/nano-ntp.v20iS2.25 (accessed 08.10.2024).
10.Apicella A., Arpaia P., Giugliano S., Mastrati G., Moccaldi N. High-wearable EEG-based transducer for engagement detection in pediatric rehabilitation Brain-Computer Interfaces 9(3), 2022, pp.129-139 (accessed 08.10.2024).
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 (accessed 09.10.2024).
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 (accessed 09.10.2024).
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 (accessed 0.9.10.2024).
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 0.7.10.2024 - 25.10.2024).
16. B-spline curve. Available at: https://web.mit.edu/hyperbook/Patrikalakis-Maekawa-Cho/node17.html (accessed 10.10.2024).
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 20.10.2024 - 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 )