Low-complexity, multi-channel, lossless and near-lossless EEG compression
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.
| 2014 | |
| Procesamiento de Señales | |
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/41794 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522939017363456 |
|---|---|
| author | Capurro, Ignacio |
| author2 | Lecumberry, Federico Martín Menoni, Alvaro Ramírez Paulino, Ignacio Rovira, Eugenio Seroussi, Gadiel |
| author2_role | author author author author author |
| author_facet | Capurro, Ignacio Lecumberry, Federico Martín Menoni, Alvaro Ramírez Paulino, Ignacio Rovira, Eugenio Seroussi, Gadiel |
| author_role | author |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Capurro, Ignacio Lecumberry, Federico Martín Menoni, Alvaro Ramírez Paulino, Ignacio Rovira, Eugenio Seroussi, Gadiel |
| dc.date.accessioned.none.fl_str_mv | 2023-12-11T19:57:48Z |
| dc.date.available.none.fl_str_mv | 2023-12-11T19:57:48Z |
| dc.date.issued.es.fl_str_mv | 2014 |
| dc.date.submitted.es.fl_str_mv | 20231211 |
| dc.description.abstract.none.fl_txt_mv | 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. |
| dc.description.es.fl_txt_mv | Trabajo presentado en 22nd European Signal Processing Conference, Lisboa, Portugal, 2014 |
| dc.identifier.citation.es.fl_str_mv | 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. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/41794 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.other.es.fl_str_mv | Procesamiento de Señales |
| dc.title.none.fl_str_mv | Low-complexity, multi-channel, lossless and near-lossless EEG compression |
| dc.type.es.fl_str_mv | Ponencia |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/conferenceObject |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Trabajo presentado en 22nd European Signal Processing Conference, Lisboa, Portugal, 2014 |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | COLIBRI_2ed7a1dca042b833a3ba87be9a07fc52 |
| identifier_str_mv | 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. |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/41794 |
| publishDate | 2014 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | 2023-12-11T19:57:48Z2023-12-11T19:57:48Z201420231211Capurro, 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.https://hdl.handle.net/20.500.12008/41794Trabajo presentado en 22nd European Signal Processing Conference, Lisboa, Portugal, 2014Current 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.Made available in DSpace on 2023-12-11T19:57:48Z (GMT). No. of bitstreams: 5 low-complexity2014.pdf: 305072 bytes, checksum: 1dfc9f0b0bbe3bd7f2fcd82902ace537 (MD5) license_text: 21936 bytes, checksum: 9833653f73f7853880c94a6fead477b1 (MD5) license_url: 49 bytes, checksum: 4afdbb8c545fd630ea7db775da747b2f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) license.txt: 4244 bytes, checksum: 528b6a3c8c7d0c6e28129d576e989607 (MD5) Previous issue date: 2014enengLas obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad De La República. (Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)Procesamiento de SeñalesLow-complexity, multi-channel, lossless and near-lossless EEG compressionPonenciainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaCapurro, IgnacioLecumberry, FedericoMartín Menoni, AlvaroRamírez Paulino, IgnacioRovira, EugenioSeroussi, GadielProcesamiento de SeñalesTratamiento de 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- Universidad de la Repúblicafalse |
| spellingShingle | Low-complexity, multi-channel, lossless and near-lossless EEG compression Capurro, Ignacio Procesamiento de Señales |
| status_str | publishedVersion |
| title | Low-complexity, multi-channel, lossless and near-lossless EEG compression |
| title_full | Low-complexity, multi-channel, lossless and near-lossless EEG compression |
| title_fullStr | Low-complexity, multi-channel, lossless and near-lossless EEG compression |
| title_full_unstemmed | Low-complexity, multi-channel, lossless and near-lossless EEG compression |
| title_short | Low-complexity, multi-channel, lossless and near-lossless EEG compression |
| title_sort | Low-complexity, multi-channel, lossless and near-lossless EEG compression |
| topic | Procesamiento de Señales |
| url | https://hdl.handle.net/20.500.12008/41794 |
