Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Modelling discrete states and long-term dynamics in functional brain networks.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Varying patterns of association between cortical large-scale networks and subthalamic nucleus activity in Parkinson's disease.

NPJ Parkinson's disease·2026
Same author

Modelling variability in functional brain networks using embeddings.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Canonical Hidden Markov Model Networks for studying M/EEG.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Effects of Age on Resting-State Cortical Networks.

Human brain mapping·2026
Same author

Normative modeling of brain function abnormalities in complex pathology requires a whole-brain approach.

Progress in neurobiology·2026

Related Experiment Video

Updated: May 23, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

1.6K

osl-ephys: a Python toolbox for the analysis of electrophysiology data.

Mats W J van Es1, Chetan Gohil1,2, Andrew J Quinn1,3

  • 1Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom.

Frontiers in Neuroscience
|March 10, 2025
PubMed
Summary

The OHBA Software Library (osl-ephys) offers advanced tools for analyzing electrophysiology data, enhancing reproducibility and efficiency in M/EEG research. This open-source Python package provides novel methods for sensor and source space analysis.

Keywords:
M/EEGMNE-Pythonanalysiselectroencephalography (EEG)electrophysiologymagnetoencephalography (MEG)pythontoolbox

More Related Videos

High-Throughput Analysis of Optical Mapping Data Using ElectroMap
07:36

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

9.2K
Author Spotlight: Epimysial Electrode Fabrication and Testing in ACL Injury Studies
04:48

Author Spotlight: Epimysial Electrode Fabrication and Testing in ACL Injury Studies

Published on: April 12, 2024

372

Related Experiment Videos

Last Updated: May 23, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

1.6K
High-Throughput Analysis of Optical Mapping Data Using ElectroMap
07:36

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

9.2K
Author Spotlight: Epimysial Electrode Fabrication and Testing in ACL Injury Studies
04:48

Author Spotlight: Epimysial Electrode Fabrication and Testing in ACL Injury Studies

Published on: April 12, 2024

372

Area of Science:

  • Neuroscience
  • Computational Biology
  • Biomedical Engineering

Background:

  • Electrophysiology data analysis, particularly for magneto-/electro-encephalography (M/EEG), requires robust and reproducible tools.
  • Existing toolboxes may lack specific functionalities or efficient processing capabilities for large datasets.

Purpose of the Study:

  • Introduce the OHBA Software Library for electrophysiology data analysis (osl-ephys).
  • Provide a modular toolbox that extends MNE-Python with unique M/EEG analysis capabilities.
  • Facilitate reproducible and efficient processing of large-scale electrophysiology datasets.

Main Methods:

  • Developed osl-ephys as a Python package, building upon MNE-Python.
  • Implemented batch parallel processing for efficient data handling.
  • Introduced a config API, log keeping, and HTML processing reports for reproducibility and quality assurance.
  • Developed new functionalities for volumetric coregistration, source reconstruction, and parcellation.

Main Results:

  • Demonstrated the application of osl-ephys using a publicly available M/EEG dataset (multimodal faces dataset).
  • Showcased modularity and unique analysis tools for sensor and source space.
  • Highlighted efficient processing of large data volumes and high standards for reproducibility.
  • Presented an alternative pipeline for volumetric analysis, avoiding surface-based processing.

Conclusions:

  • osl-ephys provides a comprehensive, open-source solution for advanced M/EEG data analysis.
  • The library enhances reproducibility, efficiency, and quality assurance in electrophysiology research.
  • Offers novel volumetric analysis capabilities as an alternative to surface-based methods.