STEAM-EEG: Spatiotemporal EEG Analysis with Markov Transfer Fields and Attentive CNNs
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913634966306816 |
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| author | Qin, Jiahao Liu, Feng |
| author_facet | Qin, Jiahao Liu, Feng |
| contents | Electroencephalogram (EEG) signals play a pivotal role in biomedical research and clinical applications, including epilepsy diagnosis, sleep disorder analysis, and brain-computer interfaces. However, the effective analysis and interpretation of these complex signals often present significant challenges. This paper presents a novel approach that integrates computer graphics techniques with biological signal pattern recognition, specifically using Markov Transfer Fields (MTFs) for EEG time series imaging. The proposed framework (STEAM-EEG) employs the capabilities of MTFs to capture the spatiotemporal dynamics of EEG signals, transforming them into visually informative images. These images are then rendered, visualised, and modelled using state-of-the-art computer graphics techniques, thereby facilitating enhanced data exploration, pattern recognition, and decision-making. The code could be accessed from GitHub. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01959 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | STEAM-EEG: Spatiotemporal EEG Analysis with Markov Transfer Fields and Attentive CNNs Qin, Jiahao Liu, Feng Computer Vision and Pattern Recognition Artificial Intelligence Computational Engineering, Finance, and Science Electroencephalogram (EEG) signals play a pivotal role in biomedical research and clinical applications, including epilepsy diagnosis, sleep disorder analysis, and brain-computer interfaces. However, the effective analysis and interpretation of these complex signals often present significant challenges. This paper presents a novel approach that integrates computer graphics techniques with biological signal pattern recognition, specifically using Markov Transfer Fields (MTFs) for EEG time series imaging. The proposed framework (STEAM-EEG) employs the capabilities of MTFs to capture the spatiotemporal dynamics of EEG signals, transforming them into visually informative images. These images are then rendered, visualised, and modelled using state-of-the-art computer graphics techniques, thereby facilitating enhanced data exploration, pattern recognition, and decision-making. The code could be accessed from GitHub. |
| title | STEAM-EEG: Spatiotemporal EEG Analysis with Markov Transfer Fields and Attentive CNNs |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2501.01959 |