Compression and reconstruction methodology for neural signals based on patch ordering inpainting for brain monitoring
Sebastian Schmale, Heiner Lange, Benjamin Knoop, Dagmar Peters-Drolshagen, Steffen Paul
Abstract
The aim of this study is to present the first compression and reconstruction methodology based on patch ordering inpainting algorithm for monitoring neural activity. This novel in-painting approach is especially important for the technical realization of implantable neural measurement systems (NMS) since they are subject to strict resource limitations as area and energy consumption. Intersection masks with center square as well as random-based masks are utilized for suitable neural data compression considering the patch ordering inpainting. The proposed inpainting methodology outperforms the structure-based inpainting algorithm and often applied Compressed Sensing strategy with regard to reconstruction quality of the real measured neural signals. These algorithms focus on complexity reduction according to hardware on implantable NMS. At high degrees of compression, the patch ordering inpainting yields well-suited or equal reconstruction results in contrast to JPEG or JPEG2000, respectively.
BibTeX
@inproceedings{icassp2016_compressionandre,
title = {Compression and reconstruction methodology for neural signals based on patch ordering inpainting for brain monitoring},
author = {Sebastian Schmale and Heiner Lange and Benjamin Knoop and Dagmar Peters-Drolshagen and Steffen Paul},
booktitle = {ICASSP 2016},
year = {2016}
}