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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2410.11844 |
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| _version_ | 1866929546035462144 |
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| author | Bernadotte, Alexandra Buchstaber, Victor |
| author_facet | Bernadotte, Alexandra Buchstaber, Victor |
| contents | This paper provides a brief introduction of the mathematical theory behind the time series unfolding method.
The algorithms presented serve as a valuable mathematical and analytical tool for analyzing data collected from brain-computer interfaces.
In our study, we implement a mathematical model based on polyharmonic signals to interpret the data from brain-computer interface sensors.
The analysis of data coming to the brain-computer interface sensors is based on a mathematical model of the signal in the form of a polyharmonic signal.
Our main focus is on addressing the problem of evaluating the number of sources, or active brain oscillators.
The efficiency of our approach is demonstrated through analysis of data recorded from a non-invasive brain-computer interface developed by the author. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11844 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Method for Evaluating the Number of Signal Sources and Application to Non-invasive Brain-computer Interface Bernadotte, Alexandra Buchstaber, Victor Neurons and Cognition Computer Vision and Pattern Recognition Human-Computer Interaction Signal Processing This paper provides a brief introduction of the mathematical theory behind the time series unfolding method. The algorithms presented serve as a valuable mathematical and analytical tool for analyzing data collected from brain-computer interfaces. In our study, we implement a mathematical model based on polyharmonic signals to interpret the data from brain-computer interface sensors. The analysis of data coming to the brain-computer interface sensors is based on a mathematical model of the signal in the form of a polyharmonic signal. Our main focus is on addressing the problem of evaluating the number of sources, or active brain oscillators. The efficiency of our approach is demonstrated through analysis of data recorded from a non-invasive brain-computer interface developed by the author. |
| title | Method for Evaluating the Number of Signal Sources and Application to Non-invasive Brain-computer Interface |
| topic | Neurons and Cognition Computer Vision and Pattern Recognition Human-Computer Interaction Signal Processing |
| url | https://arxiv.org/abs/2410.11844 |