OPM-FLUX Toolkit
Learn how to analyse OPM-MEG data
21-22 July 2025
Toolkit Summary
The OPM-FLUX toolkit course provides a comprehensive introduction to analysing OPM-MEG data using OPM-FLUX, an advanced analysis pipeline for human electrophysiological data. Originally designed for SQUID-based MEG, the FLUX pipeline has been adapted for OPM-MEG. While OPM-MEG offers exciting opportunities in cognitive and clinical neuroscience, analysing its data can be challenging due to technical complexities and a lack of standardised methods. To meet the increasing demand for accessible training in this area, we have developed the FLUX toolkit, built on MNE-Python, a leading MEG analysis toolbox. Participants will be taught the theoretical foundations of OPM sensors and human electrophysiology and gain practical experience with OPM FLUX.
In the mornings we will have lectures on the basics data analysis as well as examples on novel cognitive neuroscience research based MEG or OPM-MEG. In the afternoon we will have hands-on data analysis workshops applying the OPM-FLUX pipeline on a data set collected from the local OPM systems. Will cover the analysis of FieldLine as well Cerca/QuSpin data. The analysis sections will include the BIDS format, preprocessing, event-related fields, spectral analysis, source modelling, and multivariate pattern analysis. We will not provide hands-on training on the actual data acquisition but we will outline the general steps required.
Location
TBA
Intended audience
The target audience is users new to OPM-MEG with the intend of integrating the technique in cognitive and clinical neuroscience. The OPM-FLUX pipeline is meant to provide a standardised approach for using MNE Python to analyse OPM-MEG data. To partake in the course, basic Python skills are required. For users with more advanced skills we recommend using the OHBA Software Library which also is associated with a course.
Requirements
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Fundamental knowledge of Python
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A laptop (Windows/MacOS) w/ a 16Gb
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MNE-Python installed as tested
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Ideally Jupyter w/ MNE-Python linked
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Test-dataset downloaded ~11Gb
Before arriving
It is essential you have installed and tested the software+data before arriving. In case of trouble please seek local support.
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Bring a MacOS or Windows laptop ideally with 16 GB or more of memory.
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Make sure to have Python installed (version 3.8-3.11)
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Install MNE Python
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Test that you can run MNE Python from Jupyter Notebook
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Download the OPM MEG data (its ~11GB so please use fast connection) [[WILL BE UPDATED A FEW DAYS BEFORE TOOLKIT]]
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The website for the toolbox is here FLUX pipeline for Cerca/QuSpin OPM data but it is also possible to reach (and/or clone) the scripts directly from the GitHub repository
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We recommend installing a tool like GitHub Desktop to clone the FLUX pipeline locally from the GitHub repository
Registration and deadlines
TBA
Schedule
Day 1
9:00-10:00 Lectures
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The physiological basis of MEG
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The basic of OPM and SQUID MEG hardware
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Running an experiment.
10:00-10:15
10:15-12:15 Hands-on
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Artefact attenuation by HFC
12:15-13:00 Lunch
13:00 - 14:00 Lectures
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The MEG-BIDS format
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Algorithms for noise artefact reduction (ICA, SSP, HFC, AMM, ...)
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Preprocessing & Event-related Fields
14:00-14:15 Break
14:15-16:30 Hands-on
Day 2
9:00-10:00 Lecture
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Spectral analysis and time-frequency representations of power
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Multi-variate pattern analysis
10:00-10:15 Break
10:15-12:15 Hands-on
12:15-13:00 Lunch
13:00-14:00 Lecture
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The forward model
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Source modelling
14:00-14:15 Break
14:30-16:30 Hands-on
Recommended Reading Materials
(ask us for PDFs you cannot find)
MEG and OPM instrumentation:
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Hari, R. (2004) Magnetoencephalography in Clinical Neurophysiological Assessment of Human Cortical Functions. in Niedermeyer's Electroencephalography : Basic Principles, Clinical Applications, and Related Fields by Schomer and Lopes da Silva
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Hämäläinen, M., Hari, R., Ilmoniemi, R.J., , Knuutila, J., and Lounasmaa, O.V. (1993) Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain.
Reviews of modern Physics 65 (2), 413 -
Brookes, M.J., Leggett, J., Rea, M., Hill, R.M., Holmes, N., Boto, E., Bowtell, R. (2022) Magnetoencephalography with optically pumped magnetometers (OPM-MEG): the next generation of functional neuroimaging. Trends Neurosci. 45(8):621-634. doi: 10.1016/j.tins.2022.05.008.
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Kowalczyk, A.U., Bezsudnova, Y., Jensen, O., and Barontini, G. (2021) Detection of human auditory evoked brain signals with a resilient nonlinear optically pumped magnetometer. Neuroimage. 226:117497. doi: 10.1016/j.neuroimage.2020.117497.
Physiology:
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Lopes da Silva, F.H. (2010) Electrophysiological Basis of MEG Signals. In MEG: An Introduction to Methods, Eds. Hansen, Kringelback and Salmelin, Oxford Academic Books
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Buzsáki, G., Anastassiou, C.A., and Koch, C. (2012) The origin of extracellular fields and currents--EEG, ECoG, LFP and spikes. Nat Rev Neurosci. 2012 May 18;13(6):407-20. doi: 10.1038/nrn3241.
Event-related responses in cognition
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Woodman G.F. (2010) A brief introduction to the use of event-related potentials in studies of perception and attention. Atten Percept Psychophys. 72(8):2031-46.
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Luck, S.J. (2014) An Introduction to the Event-Related Potential Technique, second edition. The MIT Press
Oscillations in cognition:
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Jensen, O., and Hanslmayr (2020) The Role of Alpha Oscillations for Attention and Working Memory. In: The Cognitive Neurosciences. The MIT Press
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Jensen, O., Spaak, E., and Zumer, J.M. (2014) Human Brain Oscillations: From Physiological Mechanisms to Analysis and Cognition. In Magnetoencephalography. Eds. Supek and Aine. Springer
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Jensen, O. (2024) Distractor inhibition by alpha oscillations is controlled by an indirect mechanism governed by goal-relevant information. Commun Psychol. 2024;2(1):36. doi: 10.1038/s44271-024-00081-w.
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Buzsaki, G. (2011) Rhythms of the Brain. Oxford University Press
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Jensen, O. (2023) Rhythms and Cognition. Brain Inspired 160. Podcast
Spectral analysis
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Smith, S.W. (1997) The Discrete Fourier Transform. Chapter 8. In The Scientist and Engineer's Guide to DSP. PDF
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Smith, S.W. (1997) Applications of the DFT. Chapter 9. In The Scientist and Engineer's Guide to DSP. PDF
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Bruns, A. (2004) Fourier-, Hilbert- and wavelet-based signal analysis: are they really different approaches? J Neurosci Methods. 137(2):321-32. doi: 10.1016/j.jneumeth.2004.03.002.
Multi-variate pattern analysis
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Cichy, R.M., Pantazis, D., and Oliva, A. (2014) Resolving human object recognition in space and time, Nature Neuroscience, 17:455–462.
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Guggenmos, M., Sterzer, P., and Cichy, R.M. (2018) Multivariate pattern analysis for MEG: A comparison of dissimilarity measures, NeuroImage, 173:434-447.
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King, J.R., and Dehaene, S. (2014) Characterizing the dynamics of mental representations: the temporal generalization method, Trends in Cognitive Sciences, 18(4): 203-210
Source modelling
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Baillet, S. (2017) Magnetoencephalography for brain electrophysiology and imaging. Nat Neurosci. 20(3):327-339. doi: 10.1038/nn.4504.
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Baillet S, Mosher JC, Leahy RM (2001) Electromagnetic brain mapping, IEEE SP MAG .
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Dale, A.M., Liu, A.K., Fischl, B.R., Buckner, R.L., Belliveau, J.W., Lewine, J.D., and Halgren, E. (2000) Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron 2000 Apr, 26(1):55-67
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Van Veen,B.D., Van Drongelen, W., Yuchtman, M. and Suzuki, A. (1997) Localization of brain electrical activity via linearly constrained minimum variance spatial filtering, IEEE Transactions on Biomedical Engineering 44(9) 867-880
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Jensen, O., and Hesse, C. (2001) Estimating Distributed Representation of Evoked Responses and Oscillatory Brain Activity. In MEG: An Introduction to Methods, Eds. Hansen, Kringelback and Salmelin, Oxford Academic Books