@InProceedings{6952756,
  author = {S. Schmale and J. Hoeffmann and B. Knoop and G. Kreiselmeyer and H. Hamer and D. Peters-Drolshagen and S. Paul},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Exploiting correlation in neural signals for data compression},
  year = {2014},
  pages = {2080-2084},
  abstract = {Progress in invasive brain research relies on signal acquisition at high temporal- and spatial resolutions, resulting in a data deluge at the (wireless) interface to the external world. Hence, data compression at the implant site is necessary in order to comply with the neurophysiological restrictions, especially when it comes to recording and transmission of neural raw data. This work investigates spatial correlations of neural signals, leading to a significant increase in data compression with a suitable sparse signal representation before the wireless data transmission at the implant site. Subsequently, we used the correlation-aware two-dimensional DCT used in image processing, to exploit spatial correlation of the data set. In order to guarantee a certain sparsity in the signal representation, two paradigms of zero forcing are evaluated and applied: Significant coefficients- and block sparsity-zero forcing.},
  keywords = {brain;compressed sensing;data compression;discrete cosine transforms;image coding;image representation;medical image processing;neurophysiology;prosthetics;invasive brain research;signal acquisition;high temporal-resolution;high spatial resolution;data compression;implant site;neurophysiological restrictions;neural raw data;neural signals;sparse signal representation;wireless data transmission;correlation-aware two-dimensional DCT;image processing;coefficient-zero forcing;block sparsity-zero forcing;discrete cosine transform;Correlation;Abstracts;Electrodes;Neural Signals;Correlation;Data Compression;Compressed Sensing;Sparse Coding},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569922747.pdf},
}

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Hence, data compression at the implant site is necessary in order to comply with the neurophysiological restrictions, especially when it comes to recording and transmission of neural raw data. This work investigates spatial correlations of neural signals, leading to a significant increase in data compression with a suitable sparse signal representation before the wireless data transmission at the implant site. Subsequently, we used the correlation-aware two-dimensional DCT used in image processing, to exploit spatial correlation of the data set. In order to guarantee a certain sparsity in the signal representation, two paradigms of zero forcing are evaluated and applied: Significant coefficients- and block sparsity-zero forcing.","keywords":"brain;compressed sensing;data compression;discrete cosine transforms;image coding;image representation;medical image processing;neurophysiology;prosthetics;invasive brain research;signal acquisition;high temporal-resolution;high spatial resolution;data compression;implant site;neurophysiological restrictions;neural raw data;neural signals;sparse signal representation;wireless data transmission;correlation-aware two-dimensional DCT;image processing;coefficient-zero forcing;block sparsity-zero forcing;discrete cosine transform;Correlation;Abstracts;Electrodes;Neural Signals;Correlation;Data Compression;Compressed Sensing;Sparse Coding","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569922747.pdf","bibtex":"@InProceedings{6952756,\n author = {S. Schmale and J. Hoeffmann and B. Knoop and G. Kreiselmeyer and H. Hamer and D. Peters-Drolshagen and S. Paul},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {Exploiting correlation in neural signals for data compression},\n year = {2014},\n pages = {2080-2084},\n abstract = {Progress in invasive brain research relies on signal acquisition at high temporal- and spatial resolutions, resulting in a data deluge at the (wireless) interface to the external world. Hence, data compression at the implant site is necessary in order to comply with the neurophysiological restrictions, especially when it comes to recording and transmission of neural raw data. This work investigates spatial correlations of neural signals, leading to a significant increase in data compression with a suitable sparse signal representation before the wireless data transmission at the implant site. Subsequently, we used the correlation-aware two-dimensional DCT used in image processing, to exploit spatial correlation of the data set. In order to guarantee a certain sparsity in the signal representation, two paradigms of zero forcing are evaluated and applied: Significant coefficients- and block sparsity-zero forcing.},\n keywords = {brain;compressed sensing;data compression;discrete cosine transforms;image coding;image representation;medical image processing;neurophysiology;prosthetics;invasive brain research;signal acquisition;high temporal-resolution;high spatial resolution;data compression;implant site;neurophysiological restrictions;neural raw data;neural signals;sparse signal representation;wireless data transmission;correlation-aware two-dimensional DCT;image processing;coefficient-zero forcing;block sparsity-zero forcing;discrete cosine transform;Correlation;Abstracts;Electrodes;Neural Signals;Correlation;Data Compression;Compressed Sensing;Sparse Coding},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569922747.pdf},\n}\n\n","author_short":["Schmale, S.","Hoeffmann, J.","Knoop, B.","Kreiselmeyer, G.","Hamer, H.","Peters-Drolshagen, D.","Paul, S."],"key":"6952756","id":"6952756","bibbaseid":"schmale-hoeffmann-knoop-kreiselmeyer-hamer-petersdrolshagen-paul-exploitingcorrelationinneuralsignalsfordatacompression-2014","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569922747.pdf"},"keyword":["brain;compressed sensing;data compression;discrete cosine transforms;image coding;image representation;medical image processing;neurophysiology;prosthetics;invasive brain research;signal acquisition;high temporal-resolution;high spatial resolution;data compression;implant site;neurophysiological restrictions;neural raw data;neural signals;sparse signal representation;wireless data transmission;correlation-aware two-dimensional DCT;image processing;coefficient-zero forcing;block sparsity-zero forcing;discrete cosine transform;Correlation;Abstracts;Electrodes;Neural Signals;Correlation;Data Compression;Compressed Sensing;Sparse Coding"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2014url.bib","creationDate":"2021-02-13T17:43:41.754Z","downloads":0,"keywords":["brain;compressed sensing;data compression;discrete cosine transforms;image coding;image representation;medical image processing;neurophysiology;prosthetics;invasive brain research;signal acquisition;high temporal-resolution;high spatial resolution;data compression;implant site;neurophysiological restrictions;neural raw data;neural signals;sparse signal representation;wireless data transmission;correlation-aware two-dimensional dct;image processing;coefficient-zero forcing;block sparsity-zero forcing;discrete cosine transform;correlation;abstracts;electrodes;neural signals;correlation;data compression;compressed sensing;sparse coding"],"search_terms":["exploiting","correlation","neural","signals","data","compression","schmale","hoeffmann","knoop","kreiselmeyer","hamer","peters-drolshagen","paul"],"title":"Exploiting correlation in neural signals for data compression","year":2014,"dataSources":["A2ezyFL6GG6na7bbs","oZFG3eQZPXnykPgnE"]}