The osl-ephys Python toolbox provides an efficient, reproducible, and modular pipeline for analyzing electrophysiology data, reducing batch processing time from 175 minutes to 21 minutes.
The osl-ephys toolbox provides an efficient, reproducible, and scalable Python-based pipeline for M/EEG data analysis, building upon MNE-Python.
Absolute Event Rate: 21% vs 175%
We describe OHBA Software Library for the analysis of electrophysiology data (osl-ephys). This toolbox builds on top of the widely used MNE-Python package and provides unique analysis tools for magneto-/electro-encephalography (M/EEG) sensor and source space analysis, which can be used modularly. In particular, it facilitates processing large amounts of data using batch parallel processing, with high standards for reproducibility through a config API and log keeping, and efficient quality assurance by producing HTML processing reports. It also provides new functionality for doing coregistration, source reconstruction and parcellation in volumetric space, allowing for an alternative pipeline that avoids the need for surface-based processing, e.g., through the use of Fieldtrip. Here, we introduce osl-ephys by presenting examples applied to a publicly available M/EEG data (the multimodal faces dataset). osl-ephys is open-source software distributed on the Apache License and available as a Python package through PyPi and GitHub.
Es et al. (Fri,) reported a other. osl-ephys toolbox vs. MNE-Python pipeline was evaluated on Processing duration. The osl-ephys Python toolbox provides an efficient, reproducible, and modular pipeline for analyzing electrophysiology data, reducing batch processing time from 175 minutes to 21 minutes.