EEG seizure classification with temporal spiking neural networks and mutual information-based feature selection
Abstract Epilepsy affects over 50 million individuals worldwide, necessitating accurate and energy-efficient seizure detection systems. While Electroencephalography (EEG) is the clinical gold standard, automated classification faces challenges from high-dimensional, noisy, and non-stationary data. T...
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| Autori principali: | , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
SpringerOpen
2025-11-01
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| Serie: | Journal of Engineering and Applied Science |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1186/s44147-025-00796-5 |
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