Wearable EEG via lossless compression
Resumen:
This work presents a wearable multi-channel EEG recording system featuring a lossless compression algorithm. The algorithm, based in a previously reported algorithm by the authors, exploits the existing temporal correlation between samples at different sampling times, and the spatial correlation between different electrodes across the scalp. The low-power platform is able to compress, by a factor between 2.3 and 3.6, up to 300sps from 64 channels with a power consumption of 176μW/ch. The performance of the algorithm compares favorably with the best compression rates reported up to date in the literature.
| 2016 | |
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Electroencephalography Random access memory Compression algorithms Power demand Microcontrollers Prediction algorithms Correlation Data compression Humans |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/23862 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522895739486208 |
|---|---|
| author | Dufort, Guillermo |
| author2 | Favaro, Federico Lecumberry, Federico Martín, Álvaro Oliver, Juan Pablo Oreggioni, Julián Ramírez, Ignacio Seroussi, Gadiel Steinfeld, Leonardo |
| author2_role | author author author author author author author author |
| author_facet | Dufort, Guillermo Favaro, Federico Lecumberry, Federico Martín, Álvaro Oliver, Juan Pablo Oreggioni, Julián Ramírez, Ignacio Seroussi, Gadiel Steinfeld, Leonardo |
| author_role | author |
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| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | Dufort Guillermo, Universidad de la República (Uruguay). Facultad de Ingeniería. Favaro Federico, Universidad de la República (Uruguay). Facultad de Ingeniería. Lecumberry Federico, Universidad de la República (Uruguay). Facultad de Ingeniería. Martín Álvaro, Universidad de la República (Uruguay). Facultad de Ingeniería. Oliver Juan Pablo, Universidad de la República (Uruguay). Facultad de Ingeniería. Oreggioni Julián, Universidad de la República (Uruguay). Facultad de Ingeniería. Ramírez Ignacio, Universidad de la República (Uruguay). Facultad de Ingeniería. Seroussi Gadiel, Universidad de la República (Uruguay). Facultad de Ingeniería. Steinfeld Leonardo, Universidad de la República (Uruguay). Facultad de Ingeniería. |
| dc.creator.none.fl_str_mv | Dufort, Guillermo Favaro, Federico Lecumberry, Federico Martín, Álvaro Oliver, Juan Pablo Oreggioni, Julián Ramírez, Ignacio Seroussi, Gadiel Steinfeld, Leonardo |
| dc.date.accessioned.none.fl_str_mv | 2020-05-06T23:24:28Z |
| dc.date.available.none.fl_str_mv | 2020-05-06T23:24:28Z |
| dc.date.issued.none.fl_str_mv | 2016 |
| dc.description.abstract.none.fl_txt_mv | This work presents a wearable multi-channel EEG recording system featuring a lossless compression algorithm. The algorithm, based in a previously reported algorithm by the authors, exploits the existing temporal correlation between samples at different sampling times, and the spatial correlation between different electrodes across the scalp. The low-power platform is able to compress, by a factor between 2.3 and 3.6, up to 300sps from 64 channels with a power consumption of 176μW/ch. The performance of the algorithm compares favorably with the best compression rates reported up to date in the literature. |
| dc.format.extent.es.fl_str_mv | 4 p. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | Dufort, G., Favaro, F., Lecumberry, F., y otros. Wearable EEG via lossless compression [Preprint] Publicado en : IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society. Orlando, Florida, 16-20 aug., 2016. DOI: 10.1109/EMBC.2016.7591116 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/23862 |
| dc.language.iso.none.fl_str_mv | en_US eng |
| dc.publisher.es.fl_str_mv | IEEE |
| dc.relation.ispartof.es.fl_str_mv | IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), Orlando, Florida, USA, 16-20 aug,, 2016. p.1995-1998. |
| 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.es.fl_str_mv | Electroencephalography Random access memory Compression algorithms Power demand Microcontrollers Prediction algorithms Correlation Data compression Humans |
| dc.title.none.fl_str_mv | Wearable EEG via lossless compression |
| dc.type.es.fl_str_mv | Preprint |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/preprint |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/submittedVersion |
| description | This work presents a wearable multi-channel EEG recording system featuring a lossless compression algorithm. The algorithm, based in a previously reported algorithm by the authors, exploits the existing temporal correlation between samples at different sampling times, and the spatial correlation between different electrodes across the scalp. The low-power platform is able to compress, by a factor between 2.3 and 3.6, up to 300sps from 64 channels with a power consumption of 176μW/ch. The performance of the algorithm compares favorably with the best compression rates reported up to date in the literature. |
| eu_rights_str_mv | openAccess |
| format | preprint |
| id | COLIBRI_e17ece2e7056caf2dd2db39b5b9bead5 |
| identifier_str_mv | Dufort, G., Favaro, F., Lecumberry, F., y otros. Wearable EEG via lossless compression [Preprint] Publicado en : IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society. Orlando, Florida, 16-20 aug., 2016. DOI: 10.1109/EMBC.2016.7591116 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en_US |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/23862 |
| publishDate | 2016 |
| 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 | Dufort Guillermo, Universidad de la República (Uruguay). Facultad de Ingeniería.Favaro Federico, Universidad de la República (Uruguay). Facultad de Ingeniería.Lecumberry Federico, Universidad de la República (Uruguay). Facultad de Ingeniería.Martín Álvaro, Universidad de la República (Uruguay). Facultad de Ingeniería.Oliver Juan Pablo, Universidad de la República (Uruguay). Facultad de Ingeniería.Oreggioni Julián, Universidad de la República (Uruguay). Facultad de Ingeniería.Ramírez Ignacio, Universidad de la República (Uruguay). Facultad de Ingeniería.Seroussi Gadiel, Universidad de la República (Uruguay). Facultad de Ingeniería.Steinfeld Leonardo, Universidad de la República (Uruguay). Facultad de Ingeniería.2020-05-06T23:24:28Z2020-05-06T23:24:28Z2016Dufort, G., Favaro, F., Lecumberry, F., y otros. Wearable EEG via lossless compression [Preprint] Publicado en : IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society. Orlando, Florida, 16-20 aug., 2016. DOI: 10.1109/EMBC.2016.7591116https://hdl.handle.net/20.500.12008/23862This work presents a wearable multi-channel EEG recording system featuring a lossless compression algorithm. The algorithm, based in a previously reported algorithm by the authors, exploits the existing temporal correlation between samples at different sampling times, and the spatial correlation between different electrodes across the scalp. The low-power platform is able to compress, by a factor between 2.3 and 3.6, up to 300sps from 64 channels with a power consumption of 176μW/ch. The performance of the algorithm compares favorably with the best compression rates reported up to date in the literature.Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2020-05-06T20:12:48Z No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) DFLMOORSS16.pdf: 703936 bytes, checksum: 2b1d995f89ee1eb972a5188ec48c741f (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2020-05-06T22:17:02Z (GMT) No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) DFLMOORSS16.pdf: 703936 bytes, checksum: 2b1d995f89ee1eb972a5188ec48c741f (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@fic.edu.uy) on 2020-05-06T23:24:28Z (GMT). No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) DFLMOORSS16.pdf: 703936 bytes, checksum: 2b1d995f89ee1eb972a5188ec48c741f (MD5) Previous issue date: 20164 p.application/pdfen_USengIEEEIEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), Orlando, Florida, USA, 16-20 aug,, 2016. p.1995-1998.Las 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)ElectroencephalographyRandom access memoryCompression algorithmsPower demandMicrocontrollersPrediction algorithmsCorrelationData compressionHumansWearable EEG via lossless compressionPreprintinfo:eu-repo/semantics/preprintinfo:eu-repo/semantics/submittedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaDufort, GuillermoFavaro, FedericoLecumberry, FedericoMartín, ÁlvaroOliver, Juan PabloOreggioni, JuliánRamírez, IgnacioSeroussi, GadielSteinfeld, LeonardoElectrónicaElectrónicaElectrónicaProcesamiento de SeñalesProcesamiento de SeñalesProcesamiento de SeñalesElectrónica AplicadaMicroelectrónicaTratamiento de ImágenesElectrónica AplicadaMicroelectrónicaTratamiento de ImágenesLICENSElicense.txtlicense.txttext/plain; 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- Universidad de la Repúblicafalse |
| spellingShingle | Wearable EEG via lossless compression Dufort, Guillermo Electroencephalography Random access memory Compression algorithms Power demand Microcontrollers Prediction algorithms Correlation Data compression Humans |
| status_str | submittedVersion |
| title | Wearable EEG via lossless compression |
| title_full | Wearable EEG via lossless compression |
| title_fullStr | Wearable EEG via lossless compression |
| title_full_unstemmed | Wearable EEG via lossless compression |
| title_short | Wearable EEG via lossless compression |
| title_sort | Wearable EEG via lossless compression |
| topic | Electroencephalography Random access memory Compression algorithms Power demand Microcontrollers Prediction algorithms Correlation Data compression Humans |
| url | https://hdl.handle.net/20.500.12008/23862 |
