Low-complexity, multi-channel, lossless and near-lossless EEG compression
Ignacio Capurro, Federico Lecumberry, Alvaro Martín Menoni, Ignacio Ramírez Paulino, Eugenio Rovira, Gadiel Seroussi
Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European, 1- 5 sep 2014, Lisbon, Portugal, page 2040--2044 - 2014
Research Group(s): Tratamiento de Imagenes (gti)
Department(s): Procesamiento de Señales
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Abstract

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.

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» Ignacio Capurro
» Federico Lecumberry
» Alvaro Martín Menoni
» Ignacio Ramírez Paulino
» Eugenio Rovira
» Gadiel Seroussi