OPM-FLUX Toolkit

Learn how to analyse OPM-MEG data

9th-12th June 2025

St. Catherine's College

University of Oxford

in collaboration with

The Wellcome Centre for Integrative Neuroscience

Depart of Experimental Psychology/Department of Psychiatry

The Centre for Human Brain Health

University of Birmingham

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.

The FieldLine System at the CHBH, Birmingham

The Cerca/QuSpin system at OHBA, Oxford

Location

The seminars and hands-on session will be at the Mary Sunley Suite at St. Catherine's college at the University of Oxford. We will provide lunch and coffee as well as one dinner, but the attendees have to arrange their own accommodation, but see UniversityRooms for affordable options. We can unfortunately not offer parking at the college, but the location is easily reached by public transportation (or a 25 min walk from Oxford train station through the historic old town).

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

Each attendee is expect to bring their own laptop (Mac or Windows-PC, ideally with 16Gb RAM) with MNE Python installed and the sample dataset downloaded. We will provide instructions for installation after course acceptance.

Registration and deadlines

To sign up for the FLUX-OPM course register using the registration form by 16/05/2025. Will then select the attendees and inform them by 23/05/2025. We expect the payment of the course (£290) by 01/06/2025. This will cover lunch, 2 coffee breaks and a joint dinner in Oxford.

In the selection of attendees we aim for a diverse representation in terms of geography and gender.

Note that if you are affiliated with University of Oxford please consider instead to attend the OPM launch event 5th June at OHBA and/or the OSL course on MEG analysis 12th-14th May.

Organizing committee

Tara Ghafari

Arnab Rakshit

Ole Jensen

This project is supported by

NIHR Oxford Health Biomedical Research Centre (NIHR203316). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care

The programme

The FLUX toolkit course is divided into three sections each day. The morning sessions cover the theoretical background. After lunch we will have presentations on research highlights using OPM and MEG. The afternoons are reserved for practical, hands-on sessions during which the attendees will learn how to apply the analysis methods to a sample OPM dataset.

Monday 9/6

Monday 9th June

10:00-10:15 Meet and greet

Ole Jensen

10:15-10:45 The physiological basis of MEG

Hyojin Park

10:45-11:30 Running an OPM experiments

Arnab Rakshit

11:30-12:00 Break

12:00-13:00 The physics on OPM and the next generation of sensors

Anna Kowalczyk

13:00-14:00 Lunch

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14:00-14:30 Dissecting skilled motor control

Katja Kornysheva

14:30-15:00 OPM infant recordings

Barbara Pomiechowska

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15:00-16:00 OPM-FLUX hands-on

Setting up FLUX and MNE-Python

16:00-18:00 Visiting the OPM system at OHBA

Tuesday 10th June

10:00-10:30 Brain Imaging Data Structure (BIDS)

Tara Ghafari

10:30-11:30 Algorithms for noise artefact reduction (ICA, SSS, HFC,...)

Yulia Bezsudnova

11:30-12:00 Break

12:00-13:00 Preprocessing & Event-related Fields

Tara Ghafari

13:00-14:00 Lunch

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14:00-15:00 Spatiotemporal properties of common semantic categories for words and pictures

Yulia Bezsudnova

14:30-15:00 OPM compared to MEG for MVPA

Jiawai (Jack) Liang

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15:00-17:00 OPM-FLUX hands-on

The dataset

A first look at the data

ICA for attenuating artefacts

Wednesday 11th June

10:00-11:00 Oscillations in Cognition

Ole Jensen

11:10-12:00 The Fourier Transform

Andrew Quinn

12:00-12:15 Break

12:15-13:00 The Power Spectrum and Time Frequency Representations of power

Andrew Quinn

13:00-14:00 Lunch

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13:00-14:00 Keynote: Analysis of MEG data via hidden Markov models

Mark Woolrich

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15:00-17:00 OPM-FLUX hands-on

Education: Discrete Fourier Transform

Education: Time-frequency Representation of Power

Extracting condition-specific trials

Event-related fields

Time-frequency representations of power

19:00- Dinner in Oxford

Thursday 12th June

10:00-10:30 Forward Model

Robert Seymour

10:30-11:00 Dipole modelling and Minimum Norm Estimate

Robert Seymour

11:15-11:30 Break

11:30-12:00 Beamforming approaches

Robert Seymour

12:00- 13:00 Multivariate Pattern Analysis

Oscar Ferrante

13:00-14:00 Lunch

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14:00-14:30 Linking subcortical regions to brain oscillations

Tara Ghafari

14:30-15:00 Hierarchical processing of orthographic and semantic parafoveal information during natural reading

Lijuan Wang

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15:00-17:00 OPM-FLUX hands-on

Constructing the forward model

Source modelling using DICS beamforming

Multivariate pattern analysis

Recommended Reading Materials

(ask us for PDFs you cannot find)

MEG and OPM instrumentation:

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
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.
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:

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
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

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.
Luck, S.J. (2014) An Introduction to the Event-Related Potential Technique, second edition. The MIT Press

Oscillations in cognition:

Jensen, O., and Hanslmayr (2020) The Role of Alpha Oscillations for Attention and Working Memory. In: The Cognitive Neurosciences. The MIT Press
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
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.
Buzsaki, G. (2011) Rhythms of the Brain. Oxford University Press
Jensen, O. (2023) Rhythms and Cognition. Brain Inspired 160. Podcast

Spectral analysis

Smith, S.W. (1997) The Discrete Fourier Transform. Chapter 8. In The Scientist and Engineer's Guide to DSP. PDF
Smith, S.W. (1997) Applications of the DFT. Chapter 9. In The Scientist and Engineer's Guide to DSP. PDF
Percival, D.B. and Walden, A.T. (1993) Spectral Analysis for Physical Applications. Cambridge University Press
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

Cichy, R.M., Pantazis, D., and Oliva, A. (2014) Resolving human object recognition in space and time, Nature Neuroscience, 17:455–462.
Guggenmos, M., Sterzer, P., and Cichy, R.M. (2018) Multivariate pattern analysis for MEG: A comparison of dissimilarity measures, NeuroImage, 173:434-447.
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

Baillet, S. (2017) Magnetoencephalography for brain electrophysiology and imaging. Nat Neurosci. 20(3):327-339. doi: 10.1038/nn.4504.
Baillet S, Mosher JC, Leahy RM (2001) Electromagnetic brain mapping, IEEE SP MAG .
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
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
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