SignalPlant: an open signal processing software platform

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F16%3A00464043" target="_blank" >RIV/68081731:_____/16:00464043 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26220/16:PU120326

Výsledek na webu

<a href="http://dx.doi.org/10.1088/0967-3334/37/7/N38" target="_blank" >http://dx.doi.org/10.1088/0967-3334/37/7/N38</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/0967-3334/37/7/N38" target="_blank" >10.1088/0967-3334/37/7/N38</a>

Jazyk výsledku

angličtina

Název v původním jazyce

SignalPlant: an open signal processing software platform

Popis výsledku v původním jazyce

The growing technical standard of acquisition systems allows the acquisition of large records, often reaching gigabytes or more in size as is the case with whole-day electroencephalograph (EEG) recordings, for example. Although current 64-bit software for signal processing is able to process (e.g. filter, analyze, etc) such data, visual inspection and labeling will probably suffer from rather long latency during the rendering of large portions of recorded signals. For this reason, we have developed SignalPlant—a standalone application for signal inspection, labeling and processing. The main motivation was to supply investigators with a tool allowing fast and interactive work with large multichannel records produced by EEG, electrocardiograph and similar devices. The rendering latency was compared with EEGLAB and proves significantly faster when displaying an image from a large number of samples (e.g. 163-times faster for 75 106 samples). The presented SignalPlant software is available free and does not depend on any other computation software. Furthermore, it can be extended with plugins by thirdparties ensuring its adaptability to future research tasks and new data formats.

Název v anglickém jazyce

SignalPlant: an open signal processing software platform

Popis výsledku anglicky

The growing technical standard of acquisition systems allows the acquisition of large records, often reaching gigabytes or more in size as is the case with whole-day electroencephalograph (EEG) recordings, for example. Although current 64-bit software for signal processing is able to process (e.g. filter, analyze, etc) such data, visual inspection and labeling will probably suffer from rather long latency during the rendering of large portions of recorded signals. For this reason, we have developed SignalPlant—a standalone application for signal inspection, labeling and processing. The main motivation was to supply investigators with a tool allowing fast and interactive work with large multichannel records produced by EEG, electrocardiograph and similar devices. The rendering latency was compared with EEGLAB and proves significantly faster when displaying an image from a large number of samples (e.g. 163-times faster for 75 106 samples). The presented SignalPlant software is available free and does not depend on any other computation software. Furthermore, it can be extended with plugins by thirdparties ensuring its adaptability to future research tasks and new data formats.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

FS - Lékařská zařízení, přístroje a vybavení

OECD FORD obor

—

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Physiological Measurement

ISSN

0967-3334

e-ISSN

—

Svazek periodika

37

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

"N38"-"N48"

Kód UT WoS článku

000380814700001

EID výsledku v databázi Scopus

2-s2.0-84979992257