Título:
Low-complexity, multi-channel, lossless and near-lossless EEG compression
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Autor:
Capurro, Ignacio
Lecumberry, Federico
Martín Menoni, Alvaro
Ramírez Paulino, Ignacio
Rovira, Eugenio
Seroussi, Gadiel
Lecumberry, Federico
Martín Menoni, Alvaro
Ramírez Paulino, Ignacio
Rovira, Eugenio
Seroussi, Gadiel
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Ponencia
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Procesamiento de Señales
Año de publicación:
2014
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Resumen:
Current EEG applications imply the need for low-latency, low-power, high-fidelity data transmission and storage algorithms. This work proposes a compression algorithm meeting these requirements through the use of modern information theory and signal processing tools (such as universal coding, universal prediction, and fast online implementations of multivariate recursive least squares), combined with simple methods to exploit spatial as well as temporal redundancies typically present in EEG signals. The resulting compression algorithm requires O(1) operations per scalar sample and surpasses the current state of the art in near-lossless and lossless EEG compression ratios.
Descripción:
Trabajo presentado en 22nd European Signal Processing Conference, Lisboa, Portugal, 2014
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Citación:
Capurro, I, Lecumberry, F, Martín, A, Ramírez, I, Rovira, E, Seroussi, G. "Low-complexity, multi-channel, lossless and near-lossless EEG compression," Publicado en: Proceeding of the 22nd European Signal Processing Conference, Lisboa, Portugal, 1-5 sep. 2014, pp. 2040-2044.
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Departamento académico:
Procesamiento de Señales
Grupo de investigación:
Tratamiento de Imágenes
Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| low-complexity2014.pdf | — | 297.92 KB | Adobe PDF |