Brain-computer interaction neurointerface based on artificial intelligence and its parallel programming using high-performance calculation on cluster mobile devices
| Назва | Brain-computer interaction neurointerface based on artificial intelligence and its parallel programming using high-performance calculation on cluster mobile devices |
| Назва англійською | Brain-computer interaction neurointerface based on artificial intelligence and its parallel programming using high-performance calculation on cluster mobile devices |
| Автори | Oleh Pastukh, Vasyl Yatsyshyn |
| Принадлежність | Ternopil Ivan Puluj National Technical University, Ternopil, Ukraine |
| Бібліографічний опис | Brain-computer interaction neurointerface based on artificial intelligence and its parallel programming using high-performance calculation on cluster mobile devices / Oleh Pastukh, Vasyl Yatsyshyn // Scientific Journal of TNTU. — Tern.: TNTU, 2023. — Vol 112. — No 4. — P. 26–31. |
| Bibliographic description: | 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 TNTU (Tern.), vol 112, no 3, pp. 26–31. |
| УДК | 681.3 |
| Ключові слова | neuro interface of brain-computer interaction; artificial intelligence; parallel programming; high-performance computing, cluster mobile devices. |
The paper deals with hardware and software support for the interaction of human brain activity with the dynamic movement of the part of its upper limb based on artificial intelligence and its parallel programming using high-performance computer calculation on cluster mobile devices. The obtained results can be used as a basis for the development of high-performance software and hardware for the effective operation of brain-computer interaction neuro interfaces. | |
| ISSN: | 2522-4433 |
| Перелік літератури |
1. Musk E., Neuralink. An Integrated Brain-Machine Interface Platform With Thousands of Channels. Journal of medical Internet research. 2019. Vol. 21. No. 10. URL: https://www.jmir.org/2019/10/e16194/ (accessed: 31.10.2019).
2. Ban H., Barrett G., Borisevich A., Chaturvedi A., Dahle J., et al. Kernel Flow: a high channel countscalable TD-fNIRS system. Journal of biomedical optics. 2021. Vol. 27. No. 7. URL: https://pubmed.ncbi. nlm.nih.gov/35043610/ (accessed: 05.03.2021).
3. Andersen L. M., Jerbi K., Dalal S. S. Can EEG and MEG detect signals from the human cerebellum? Neuroimage. 2020. URL: https://pubmed.ncbi.nlm.nih.gov/32278092/ (accessed: 08.11.2020).
4. Arpaia P., Duraccio L., Moccaldi N., Rossi S. Wearable brain-computer interface instrumentation for robot-based rehabilitation by augmented reality – IEEE Transactions on Instrumentation and Measurement. 2020. Vol. 69. No. 9. P. 6362–6371.
5. Cecotti H. Adaptive time segment analysis for steady-state visual evoked potential based brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 2020. URL: https://pubmed.ncbi.nlm.nih.gov/ 31985428/ (accessed: 21.01.2020).
6. Filiz E., Arslan R.B. Design and implementation of steady state visual evoked potential based brain computer interface video game. 28th Signal Processing and Communications Applications Conference, 2020. URL: https://avesis.gsu.edu.tr/yayin/360b7f3c-ec8f-45d4-b46d-c93ed5029310/design-and-implementation-of-steady-state-visual-evoked-potential-based-brain-computer-interface-video-game (accessed: 2020).
7. Han C.-H., Müller K.-R., Hwang H.-J. Brain-switches for asynchronous brain-computer interfaces: a systematic review. Electronics. 2020. Vol. 9. No. 3. URL: https://www.mdpi.com/2079-9292/9/3/422 (accessed: 09.01.2020).
8. He H., Wu D. Different set domain adaptation for brain-computer interfaces: a label alignment approach. IEEE Trans. Neural Syst. Rehabil. Eng. 2020 Vol. 28 No. 5. URL: https://pubmed.ncbi.nlm.nih.gov/ 32167903/ (accessed: 12.03.2020)
9. Jin J., Chen Z., Xu R., Miao Y., Wang X., Jung T.-P. Developing a novel tactile p300 brain-computer interface with a cheeks-stim paradigm. IEEE Trans. Biomed. Eng. 2020. Vol. 67. No. 9. URL: https://pubmed.ncbi.nlm.nih.gov/31940515/ (accessed: 09.01.2020). 10. Jones S. R., Sliva D. D. Is alpha asymmetry a byproduct or cause of spatial attention? New evidence alpha neurofeedback controls measures of spatial attention. Neuron. 2020. Vol. 105. No. 3. URL: https:// europepmc.org/article/med/32027830 (accessed: 01.02.2020). 11. Zuo C., Jin J., Yin E., Saab R., Miao Y., Wang X., et al. Novel hybrid brain-computer interface system based on motor imagery and p300. Cogn. Neurodyn. 2020. Vol. 15. No. 2. URL: https://pubmed. ncbi.nlm.nih.gov/32226566/ (accessed: 21.10.2019). 12. Bright brain – London's eeg, neurofeedback and brain stimylation centre. URL: https://www. brightbraincentre.co.uk/electroencephalogram-eeg-brainwaves/ (accessed: 07.09.2023). 13. Medic XAI. URL: https://xai-medica.com/en/equipments.html (accessed: 02.05.2023). 14. Scikit-Learn & Joblib. URL: https://ml.dask.org/joblib.html (accessed: 18.05.2023). |
| References: |
1. Musk E., Neuralink. An Integrated Brain-Machine Interface Platform With Thousands of Channels. Journal of medical Internet research. 2019. Vol. 21. No. 10. URL: https://www.jmir.org/2019/10/e16194/ (accessed: 31.10.2019).
2. Ban H., Barrett G., Borisevich A., Chaturvedi A., Dahle J., et al. Kernel Flow: a high channel countscalable TD-fNIRS system. Journal of biomedical optics. 2021. Vol. 27. No. 7. URL: https://pubmed.ncbi. nlm.nih.gov/35043610/ (accessed: 05.03.2021).
3. Andersen L. M., Jerbi K., Dalal S. S. Can EEG and MEG detect signals from the human cerebellum? Neuroimage. 2020. URL: https://pubmed.ncbi.nlm.nih.gov/32278092/ (accessed: 08.11.2020).
4. Arpaia P., Duraccio L., Moccaldi N., Rossi S. Wearable brain-computer interface instrumentation for robot-based rehabilitation by augmented reality – IEEE Transactions on Instrumentation and Measurement. 2020. Vol. 69. No. 9. P. 6362–6371.
5. Cecotti H. Adaptive time segment analysis for steady-state visual evoked potential based brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 2020. URL: https://pubmed.ncbi.nlm.nih.gov/ 31985428/ (accessed: 21.01.2020).
6. Filiz E., Arslan R.B. Design and implementation of steady state visual evoked potential based brain computer interface video game. 28th Signal Processing and Communications Applications Conference, 2020. URL: https://avesis.gsu.edu.tr/yayin/360b7f3c-ec8f-45d4-b46d-c93ed5029310/design-and-implementation-of-steady-state-visual-evoked-potential-based-brain-computer-interface-video-game (accessed: 2020).
7. Han C.-H., Müller K.-R., Hwang H.-J. Brain-switches for asynchronous brain-computer interfaces: a systematic review. Electronics. 2020. Vol. 9. No. 3. URL: https://www.mdpi.com/2079-9292/9/3/422 (accessed: 09.01.2020).
8. He H., Wu D. Different set domain adaptation for brain-computer interfaces: a label alignment approach. IEEE Trans. Neural Syst. Rehabil. Eng. 2020 Vol. 28 No. 5. URL: https://pubmed.ncbi.nlm.nih.gov/ 32167903/ (accessed: 12.03.2020)
9. Jin J., Chen Z., Xu R., Miao Y., Wang X., Jung T.-P. Developing a novel tactile p300 brain-computer interface with a cheeks-stim paradigm. IEEE Trans. Biomed. Eng. 2020. Vol. 67. No. 9. URL: https://pubmed.ncbi.nlm.nih.gov/31940515/ (accessed: 09.01.2020).
|
| Завантажити |  |