Accessing electrophysiological data from Python

Electrophysiological data is often recorded and stored in vendor specific file formats that are mostly closed and proprietary. To accommodate the need for a common and format-agnostic way to access these data from different programs, the Neuroshare API (http://neuroshare.org) was created. Its main purpose is to provide a common programming interface for application developers to access the data and metadata in a unified way. To do so, format-specific libraries ("Neuroshare libraries") are needed that are developed and maintained by the hardware manufacturers.

Here we present Python Neuroshare, an interface to the Neuroshare libraries that enables Python users to take full advantage of the Neuroshare API while benefiting from the full power of the Python programming language. Its design focuses on convenience and on integrating tightly into the programming language: For example, the data is exposed in NumPy arrays, which allows quick and easy analysis of the data. Moreover, Python Neuroshare will detect and load automatically the correct library to use for a given file, thus removing the unnecessary step of manual library selection.

Most Neuroshare libraries are provided as precompiled binaries for Microsoft Windows only. To overcome this limitation we also provide the "Neuroshare Wine Proxy" library which enables seamless use of Windows libraries on GNU/Linux and Mac OSX. Furthermore, a format conversion tool ("ns-convert") is provided that converts neuroshare-compatible files into the HDF5 file format.

Python Neuroshare is Open Source software and can be obtained as source code from the G-Node Github repository (https://github.com/G-Node/python-neuroshare). Precompiled binaries are also available via the Python Package Index. Recently, a Debian package (python-neuroshare) was created and has already been included into Debian "unstable" and "testing" distributions.

Using the Python Neuroshare library and tool suite, researchers achieve flexible and convenient cross-platform access to recorded electrophysiological data.

Supported by the German Federal Ministry of Education and Research (grant 01GQ0801).