Plesinger F, Jurco J, Halamek J, Jurak P
Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic.
Physiol Meas. 2016 Jul;37(7):N38-48. doi: 10.1088/0967-3334/37/7/N38. Epub 2016 May 31.
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 stand-alone 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 × 10(6) 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 third parties ensuring its adaptability to future research tasks and new data formats.
采集系统不断提高的技术标准使得能够采集大型记录,例如全天脑电图(EEG)记录,其大小通常能达到千兆字节甚至更多。尽管当前用于信号处理的64位软件能够处理(如滤波、分析等)此类数据,但在呈现大部分记录信号时,目视检查和标注可能会有较长延迟。因此,我们开发了SignalPlant——一款用于信号检查、标注和处理的独立应用程序。主要动机是为研究人员提供一种工具,使其能够快速且交互式地处理由脑电图、心电图及类似设备生成的大型多通道记录。将其渲染延迟与EEGLAB进行了比较,结果表明在显示大量样本的图像时,SignalPlant的速度明显更快(例如,对于75×10⁶个样本,速度快163倍)。所展示的SignalPlant软件免费提供,且不依赖任何其他计算软件。此外,第三方可以通过插件对其进行扩展,确保它能适应未来的研究任务和新的数据格式。