State Key Laboratory of Brain and Cognitive Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.
Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
Biomed Eng Online. 2017 Nov 13;16(1):129. doi: 10.1186/s12938-017-0419-7.
In a typical electrophysiological experiment, especially one that includes studying animal behavior, the data collected normally contain spikes, local field potentials, behavioral responses and other associated data. In order to obtain informative results, the data must be analyzed simultaneously with the experimental settings. However, most open-source toolboxes currently available for data analysis were developed to handle only a portion of the data and did not take into account the sorting of experimental conditions. Additionally, these toolboxes require that the input data be in a specific format, which can be inconvenient to users. Therefore, the development of a highly integrated toolbox that can process multiple types of data regardless of input data format and perform basic analysis for general electrophysiological experiments is incredibly useful.
Here, we report the development of a Python based open-source toolbox, referred to as NeoAnalysis, to be used for quick electrophysiological data processing and analysis. The toolbox can import data from different data acquisition systems regardless of their formats and automatically combine different types of data into a single file with a standardized format. In cases where additional spike sorting is needed, NeoAnalysis provides a module to perform efficient offline sorting with a user-friendly interface. Then, NeoAnalysis can perform regular analog signal processing, spike train, and local field potentials analysis, behavioral response (e.g. saccade) detection and extraction, with several options available for data plotting and statistics. Particularly, it can automatically generate sorted results without requiring users to manually sort data beforehand. In addition, NeoAnalysis can organize all of the relevant data into an informative table on a trial-by-trial basis for data visualization. Finally, NeoAnalysis supports analysis at the population level.
With the multitude of general-purpose functions provided by NeoAnalysis, users can easily obtain publication-quality figures without writing complex codes. NeoAnalysis is a powerful and valuable toolbox for users doing electrophysiological experiments.
在典型的电生理实验中，特别是包含动物行为研究的实验中，所收集的数据通常包含尖峰、局部场电位、行为反应和其他相关数据。为了获得有意义的结果，必须在实验设置的同时对数据进行分析。然而，目前大多数可用的开源数据分析工具箱仅开发用于处理部分数据，并且没有考虑到实验条件的分类。此外，这些工具箱要求输入数据采用特定格式，这可能会给用户带来不便。因此，开发一种高度集成的工具箱，可以处理多种类型的数据，而无需考虑输入数据格式，并对一般电生理实验进行基本分析，是非常有用的。
在这里，我们报告了一个基于 Python 的开源工具包的开发，称为 NeoAnalysis，用于快速电生理数据处理和分析。该工具包可以从不同的数据采集系统导入数据，而无需考虑其格式，并自动将不同类型的数据组合到一个具有标准化格式的单个文件中。在需要额外的尖峰分类的情况下，NeoAnalysis 提供了一个模块，可通过用户友好的界面进行高效的离线分类。然后，NeoAnalysis 可以进行常规的模拟信号处理、尖峰序列和局部场电位分析、行为反应（例如扫视）检测和提取，并有多种数据绘图和统计选项。特别地，它可以自动生成分类结果，而无需用户事先手动对数据进行分类。此外，NeoAnalysis 可以将所有相关数据组织到一个基于试验的有信息的表中，用于数据可视化。最后，NeoAnalysis 支持群体水平的分析。
使用 NeoAnalysis 提供的多种通用功能，用户可以轻松获得具有出版物质量的图形，而无需编写复杂的代码。NeoAnalysis 是进行电生理实验的用户的强大而有价值的工具包。
