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| Título: | Wearable EEG via lossless compression |
| Autor: | Dufort, Guillermo Favaro, Federico Lecumberry, Federico Martín, Álvaro Oliver, Juan Pablo Oreggioni, Julián Ramírez, Ignacio Seroussi, Gadiel Steinfeld, Leonardo |
| Tipo: | Preprint |
| Palabras clave: | Electroencephalography, Random access memory, Compression algorithms, Power demand, Microcontrollers, Prediction algorithms, Correlation, Data compression, Humans |
| Fecha de publicación: | 2016 |
| 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. |
| Editorial: | IEEE |
| EN: | IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), Orlando, Florida, USA, 16-20 aug,, 2016. p.1995-1998. |
| Citación: | 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 |
| Licencia: | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| Aparece en las colecciones: | Publicaciones académicas y científicas - Instituto de Ingeniería Eléctrica |
Ficheros en este ítem:
| Fichero | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| DFLMOORSS16.pdf | Preprint | 687,44 kB | Adobe PDF | Visualizar/Abrir |
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