Low-complexity, multi-channel, lossless and near-lossless EEG compression

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… (More)

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@article{Capurro2014LowcomplexityML, title={Low-complexity, multi-channel, lossless and near-lossless EEG compression}, author={Ignacio Capurro and Federico Lecumberry and Alvaro Mart{\'i}n and Ignacio Ram{\'i}rez and Eugenio Rovira and Gadiel Seroussi}, journal={2014 22nd European Signal Processing Conference (EUSIPCO)}, year={2014}, pages={2040-2044} }