Comparison of the accuracy of machine learning algorithms for brain-computer interaction based on high-performance computing technologies
| Назва | Comparison of the accuracy of machine learning algorithms for brain-computer interaction based on high-performance computing technologies |
| Назва англійською | Comparison of the accuracy of machine learning algorithms for brain-computer interaction based on high-performance computing technologies |
| Автори | Volodymyr Stefanyshyn, Ivan Stefanyshyn, Oleh Pastukh, Serhii Kulikov |
| Принадлежність | Ternopil Ivan Puluj National Technical University, Ternopil, Ukraine |
| Бібліографічний опис | Comparison of the accuracy of machine learning algorithms for brain-computer interaction based on high-performance computing technologies / Volodymyr Stefanyshyn, Ivan Stefanyshyn, Oleh Pastukh, Serhii Kulikov // Scientific Journal of TNTU. — Tern.: TNTU, 2024. — Vol 115. — No 3. — P. 82–90. |
| Bibliographic description: | Stefanyshyn V., Stefanyshyn I., Pastukh O., Kulikov S. (2024) Comparison of the accuracy of machine learning algorithms for brain-computer interaction based on high-performance computing technologies. Scientific Journal of TNTU (Tern.), vol 115, no 3, pp. 82–90. |
| DOI: | https://doi.org/10.33108/visnyk_tntu2024.03.082 |
| УДК | 539.3 |
| Ключові слова | EEG signals, neuro-interface of brain-computer interaction, artificial intelligence, parallel programming, high-performance computing, classifier, accuracy. |
In this article, we will analyze different classifiers for recognizing hand and finger movements using electroencephalograph (EEG) signals and determine which ones are the most accurate. This is important for the introduction of neurorehabilitation technologies and control of prosthetic movements. The method is based on the use of self-learning algorithms for efficient processing and analysis of informative characteristics based on EEG data. Aiming to adaptively recognize different motor commands. This ability ensures the robustness and efficiency of the system in understanding complex sets of brain signals associated with a specific motor action. The results obtained in this study demonstrate effective approaches for processing EEG signals using machine learning algorithms, analytical approaches, and cloud technologies. The perspectives revealed by this study will help to improve and speed up the development of research in the field of neurocognitive signal processing. The results obtained by us contribute to improving the work and increasing the accuracy of the interaction between the human brain and the computer. | |
| ISSN: | 2522-4433 |
| Перелік літератури |
1. Nicolelis M. The true creator of everything: How the human brain shaped the universe as we know it, 2020, 7 January, pр. 1–356.
2. Rajesh R., Rajan P. Neural networks for interference reduction in multi-track recordings. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2023-October, 2023.
3. Ajiboye A. B., Willett F. R., Young D. R., Memberg W. D., Murphy B. A., Miller J. P. & Walter B L. Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. 2017. Available at: https://pubmed.ncbi.nlm.nih.gov/ 28363483.
4. Pastukh O., Yatsyshyn V. Development of software for neuromarketing based on artificial intelligence and data science using high-performance computing and parallel programming technologies. Scientific Journal of TNTU, 2024, no. 113, pр. 143–149.
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 TNTU, 2023, no. 112, рp. 26–31.
6. Scikit-Learn & Joblib. Available at: https://ml.dask.org/joblib.html (accessed 02.04.2024).
7. Medic XAI. Available at: https://xai-medica.com/en/equipments.html (accessed 04.04.2024).
8. Crepeau EB, Cohn ES. Narrative as a Key to Understanding. In: Boyt BA, Gillen G, editors. Willard and Spackman’s Occupational Therapy. 12th ed. Wolters Kluwer/Lippincott Williams and Wilkins Health; 2013. p. 96–102.
9. Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. 2006. Available at: https://www.nature.com/articles/ nature04970.
10. Jarosiewicz B, Sarma A. A, Bacher D., Masse N. Y, Simeral J. D, Sorice B, et al. Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface. 2015. Available at: https://www.science.org/doi/10.1126/scitranslmed.aac7328.
11. Pandarinath C., Nuyujukian P., Blabe C., Sorice B., Saab J., Willett F., et al. High-performance communication by people with paralysis using an intracortical brain-computer interface. 2017. Available at: https://elifesciences.org/articles/18554.
12. Kim S., Simeral J., Hochberg L., Donoghue J., Friehs G., Black M. Point-and-Click Cursor Control With an Intracortical Neural Interface System in Humans With Tetraplegia. 2011. Available at: https://ieeexplore.ieee.org/document/5703131.
13. Bright brain – London's eeg, neurofeedback and brain stimylation centre. Available at: https://www. nejm.org/doi/10.1056/NEJMoa1608085.
14. Vansteensel M., Pels EGM, Bleichner M., Branco M., Denison T., Freudenburg Z., et al. Fully Implanted Brain-Computer Interface in a Locked-In Patient with ALS. 2016. Available at: https://www.nature.com/ articles/nature04970. 15. Serruya M., Hatsopoulos N., Paninski L., Fellows M., Donoghue J.. Instant neural control of a movement signal. 2002. Available at: https://www.nature.com/articles/416141a. |
| References: |
1. Nicolelis M. The true creator of everything: How the human brain shaped the universe as we know it, 2020, 7 January, pр. 1–356.
2. Rajesh R., Rajan P. Neural networks for interference reduction in multi-track recordings. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2023-October, 2023.
3. Ajiboye A. B., Willett F. R., Young D. R., Memberg W. D., Murphy B. A., Miller J. P. & Walter B L. Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. 2017. Available at: https://pubmed.ncbi.nlm.nih.gov/ 28363483.
4. Pastukh O., Yatsyshyn V. Development of software for neuromarketing based on artificial intelligence and data science using high-performance computing and parallel programming technologies. Scientific Journal of TNTU, 2024, no. 113, pр. 143–149.
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 TNTU, 2023, no. 112, рp. 26–31.
6. Scikit-Learn & Joblib. Available at: https://ml.dask.org/joblib.html (accessed 02.04.2024).
7. Medic XAI. Available at: https://xai-medica.com/en/equipments.html (accessed 04.04.2024).
8. Crepeau EB, Cohn ES. Narrative as a Key to Understanding. In: Boyt BA, Gillen G, editors. Willard and Spackman’s Occupational Therapy. 12th ed. Wolters Kluwer/Lippincott Williams and Wilkins Health; 2013. p. 96–102.
9. Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. 2006. Available at: https://www.nature.com/articles/ nature04970.
10. Jarosiewicz B, Sarma A. A, Bacher D., Masse N. Y, Simeral J. D, Sorice B, et al. Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface. 2015. Available at: https://www.science.org/doi/10.1126/scitranslmed.aac7328.
11. Pandarinath C., Nuyujukian P., Blabe C., Sorice B., Saab J., Willett F., et al. High-performance communication by people with paralysis using an intracortical brain-computer interface. 2017. Available at: https://elifesciences.org/articles/18554.
12. Kim S., Simeral J., Hochberg L., Donoghue J., Friehs G., Black M. Point-and-Click Cursor Control With an Intracortical Neural Interface System in Humans With Tetraplegia. 2011. Available at: https://ieeexplore.ieee.org/document/5703131.
13. Bright brain – London's eeg, neurofeedback and brain stimylation centre. Available at: https://www. nejm.org/doi/10.1056/NEJMoa1608085.
14. Vansteensel M., Pels EGM, Bleichner M., Branco M., Denison T., Freudenburg Z., et al. Fully Implanted Brain-Computer Interface in a Locked-In Patient with ALS. 2016. Available at: https://www.nature.com/ articles/nature04970. 15. Serruya M., Hatsopoulos N., Paninski L., Fellows M., Donoghue J.. Instant neural control of a movement signal. 2002. Available at: https://www.nature.com/articles/416141a. |
| Завантажити |  |