Intelligent content-aware model-free low power evoked neural signal compression

Han Chung Chen; Yu Chieh Kao; Liang Gee Chen; Fu Shan Jaw

doi:10.1007/978-3-540-89796-5_109

Intelligent content-aware model-free low power evoked neural signal compression

Han Chung Chen, Yu Chieh Kao, Liang Gee Chen, Fu Shan Jaw

Department of Biomedical Imaging and Radiological Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Neural recording is an important key for us to realize the neuron activity, and multi-channel recording will be more and more crucial. However, nowadays research can only deal with spontaneous signals, which characteristics are far different from evoked signals. For evoked signals, we cannot just judge the spike at the front-end because evoked signals can't be distinguished by recent spike sorting algorithm. Then, we need to send "full" waveform for bio-researchers. Therefore, proper compression algorithm is unavoidable due to full waveform transmission creates huge data amount. We use signal processing skills to get the targets for lossless compression, SNR>25db, and compression rate (compressed data / origin data)<25%.

Original language	English
Title of host publication	Advances in Multimedia Information Processing - PCM 2008 - 9th Pacific Rim Conference on Multimedia, Proceedings
Pages	898-901
Number of pages	4
DOIs	https://doi.org/10.1007/978-3-540-89796-5_109
State	Published - 2008
Event	9th Pacific Rim Conference on Multimedia, PCM 2008 - Tainan, Taiwan Duration: 9 Dec 2008 → 13 Dec 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5353 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th Pacific Rim Conference on Multimedia, PCM 2008
Country/Territory	Taiwan
City	Tainan
Period	9/12/08 → 13/12/08

Keywords

Evoked signals
Neural recording
Neural signal compression

Access to Document

10.1007/978-3-540-89796-5_109

Cite this

Chen, H. C., Kao, Y. C., Chen, L. G., & Jaw, F. S. (2008). Intelligent content-aware model-free low power evoked neural signal compression. In Advances in Multimedia Information Processing - PCM 2008 - 9th Pacific Rim Conference on Multimedia, Proceedings (pp. 898-901). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5353 LNCS). https://doi.org/10.1007/978-3-540-89796-5_109

Chen, Han Chung ; Kao, Yu Chieh ; Chen, Liang Gee et al. / Intelligent content-aware model-free low power evoked neural signal compression. Advances in Multimedia Information Processing - PCM 2008 - 9th Pacific Rim Conference on Multimedia, Proceedings. 2008. pp. 898-901 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{d1b32e1f20bc48f49ce162cb6706fc64,

title = "Intelligent content-aware model-free low power evoked neural signal compression",

abstract = "Neural recording is an important key for us to realize the neuron activity, and multi-channel recording will be more and more crucial. However, nowadays research can only deal with spontaneous signals, which characteristics are far different from evoked signals. For evoked signals, we cannot just judge the spike at the front-end because evoked signals can't be distinguished by recent spike sorting algorithm. Then, we need to send {"}full{"} waveform for bio-researchers. Therefore, proper compression algorithm is unavoidable due to full waveform transmission creates huge data amount. We use signal processing skills to get the targets for lossless compression, SNR>25db, and compression rate (compressed data / origin data)<25%.",

keywords = "Evoked signals, Neural recording, Neural signal compression",

author = "Chen, {Han Chung} and Kao, {Yu Chieh} and Chen, {Liang Gee} and Jaw, {Fu Shan}",

year = "2008",

doi = "10.1007/978-3-540-89796-5_109",

language = "English",

isbn = "354089795X",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "898--901",

booktitle = "Advances in Multimedia Information Processing - PCM 2008 - 9th Pacific Rim Conference on Multimedia, Proceedings",

note = "9th Pacific Rim Conference on Multimedia, PCM 2008 ; Conference date: 09-12-2008 Through 13-12-2008",

}

Chen, HC, Kao, YC, Chen, LG & Jaw, FS 2008, Intelligent content-aware model-free low power evoked neural signal compression. in Advances in Multimedia Information Processing - PCM 2008 - 9th Pacific Rim Conference on Multimedia, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5353 LNCS, pp. 898-901, 9th Pacific Rim Conference on Multimedia, PCM 2008, Tainan, Taiwan, 9/12/08. https://doi.org/10.1007/978-3-540-89796-5_109

Intelligent content-aware model-free low power evoked neural signal compression. / Chen, Han Chung; Kao, Yu Chieh; Chen, Liang Gee et al.
Advances in Multimedia Information Processing - PCM 2008 - 9th Pacific Rim Conference on Multimedia, Proceedings. 2008. p. 898-901 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5353 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Intelligent content-aware model-free low power evoked neural signal compression

AU - Chen, Han Chung

AU - Kao, Yu Chieh

AU - Chen, Liang Gee

AU - Jaw, Fu Shan

PY - 2008

Y1 - 2008

N2 - Neural recording is an important key for us to realize the neuron activity, and multi-channel recording will be more and more crucial. However, nowadays research can only deal with spontaneous signals, which characteristics are far different from evoked signals. For evoked signals, we cannot just judge the spike at the front-end because evoked signals can't be distinguished by recent spike sorting algorithm. Then, we need to send "full" waveform for bio-researchers. Therefore, proper compression algorithm is unavoidable due to full waveform transmission creates huge data amount. We use signal processing skills to get the targets for lossless compression, SNR>25db, and compression rate (compressed data / origin data)<25%.

AB - Neural recording is an important key for us to realize the neuron activity, and multi-channel recording will be more and more crucial. However, nowadays research can only deal with spontaneous signals, which characteristics are far different from evoked signals. For evoked signals, we cannot just judge the spike at the front-end because evoked signals can't be distinguished by recent spike sorting algorithm. Then, we need to send "full" waveform for bio-researchers. Therefore, proper compression algorithm is unavoidable due to full waveform transmission creates huge data amount. We use signal processing skills to get the targets for lossless compression, SNR>25db, and compression rate (compressed data / origin data)<25%.

KW - Evoked signals

KW - Neural recording

KW - Neural signal compression

UR - http://www.scopus.com/inward/record.url?scp=70350644883&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-89796-5_109

DO - 10.1007/978-3-540-89796-5_109

M3 - Conference contribution

AN - SCOPUS:70350644883

SN - 354089795X

SN - 9783540897958

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 898

EP - 901

BT - Advances in Multimedia Information Processing - PCM 2008 - 9th Pacific Rim Conference on Multimedia, Proceedings

T2 - 9th Pacific Rim Conference on Multimedia, PCM 2008

Y2 - 9 December 2008 through 13 December 2008

ER -

Chen HC, Kao YC, Chen LG, Jaw FS. Intelligent content-aware model-free low power evoked neural signal compression. In Advances in Multimedia Information Processing - PCM 2008 - 9th Pacific Rim Conference on Multimedia, Proceedings. 2008. p. 898-901. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-89796-5_109

Intelligent content-aware model-free low power evoked neural signal compression

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this