References to review papers and teaching material
Here we try to compile a list of background reading/studying material. If you know of good papers or other material, please add it by clicking on the “edit this page” link at the bottom.
If you are new to FieldTrip, please start by reading the reference paper FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. or have a look at one of the introduction videos.
EEG and MEG
Sylvain Baillet wrote a recent review manuscript on Magnetoencephalography for brain electrophysiology and imaging in Nature Neuroscience (2017).
The brain in time: insights from neuromagnetic recordings by Riitta Hari, Lauri Parkkonen and Cathy Nangini gives a comprehensive introduction to MEG.
Two somewhat older, but certainly not outdated papers on SQUID-based MEG instrumentation and signal analysis is Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain by Hämäläinen et al. (1993) and Signal Processing in Magnetoencephalography by Vrbra and Robinson (2001).
A recent review of advancements in OPM-based MEG research is given in Moving magnetoencephalography towards real-world applications with a wearable system by Boto et al. (2018).
A comprehensive introduction in the neurophysiology and biophysics of EEG (also relevant for MEG) is given in Electric Fields of the Brain: The Neurophysics of EEG, 2nd Edition by Paul L. Nunez and Ramesh Srinivasan.
Steven J Luck, An Introduction to the Event-Related Potential Technique, MIT Press: 2005, ISBN 0262621967. This book is reviewed here: Peter Hagoort (2006) Event-related potentials from the user’s perspective; Nature Neuroscience 9, 463.
Magnetoencephalography - From Signals to Dynamic Cortical Networks by Selma Supek and Cheryl J Aine (2014).
Original work on the biophysics of MEG and EEG (free ebook) Bioelectromagnetism - Principles and Applications of Bioelectric and Biomagnetic Fields by Jaakko Malmivuo & Robert Plonsey, Oxford University Press, New York, 1995.
Guidelines for acquisition, analysis and publication
Specifically for MEG we recommend the Good practice for conducting and reporting MEG research paper by Joachim Gross et al. that was published in 2012 in NeuroImage.
When doing clinical MEG, please check IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG) in Clinical Neurophysiology, 2018.
Terry Picton et al. published the Guidelines for using human event-related potentials to study cognition: recording standards and publication criteria in 2000.
The Society for Psychophysiological Research published this Committee report: publication guidelines and recommendations for studies using electroencephalography and magnetoencephalography in 2013.
The OHBM Committee on Best Practice in Data Analysis and Sharing (COBIDAS) wrote a report on Best Practices in Data Analysis and Sharing in Neuroimaging using MRI. Although this is on (f)MRI, the parts on subjects, tasks and data analysis are also relevant for EEG and MEG. The report was published in Nature neuroscience in 2017.
The International Pharmaco-EEG Society (IPEG) published their Guidelines for the Recording and Evaluation of Pharmaco-EEG Data in Man in 2012.
If you are doing clinical EEG or MEG, you should check out the Guidelines and Consensus Statements of the American Clinical Neurophysiology Society (ACNS) and the guidelines of the International Federation of Clinical Neurophysiology (IFCN). The IFCN also refers to the published Recommendations for the Practice of Clinical Neurophysiology.
Nature has a Reporting Checklist For Life Sciences Articles that is helpful to consider when you write your manuscript, also when you don’t plan to submit it to Nature.
MathWorks provides online tutorials to help you get started with the desktop and programming environment.
For an introduction to MATLAB have a look at the excellent tutorial and exercises in MATLAB for Psychologists.
Mike X. Cohen, MATLAB for Brain and Cognitive Scientists, MIT Press, 2017.
Wilson G, Aruliah DA, Brown CT, Chue Hong NP, Davis M, Guy RT, et al. (2014) Best Practices for Scientific Computing. PLoS Biol 12(1): e1001745. https://doi.org/10.1371/journal.pbio.1001745
In MATLAB you have the command window, on Linux, macOS and Windows you have the terminal and the Bash command line. With Discovering the terminal you can get a 30-minute introduction in how to use the Bash command line.
Richard Johnson has written a MATLAB style guide that explains how to write code that is more likely to be correct, understandable, sharable and maintainable.
For data sharing we recommend that you consider organizing your data along the lines of the BIDS standard. See The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments for an introduction and MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. The data2bids function helps to organize your data in the BIDS structure.
A good example for a data publication is given in A multi-subject, multi-modal human neuroimaging dataset, which includes MEG, EEG and fMRI. The dataset itself is available from OpenfMRI.
The Human Connectome Project (HCP) also provides good examples for data sharing and documentation. In Adding dynamics to the Human Connectome Project with MEG the MEG component of the HCP is described, which is available for download from the HCP website.
Analyzing Neural Time Series Data: Theory and Practice. by Mike X. Cohen.
Spectral Analysis for Physical Applications: Multitaper and Conventional Univariate Techniques Donald B. Percival and Andrew T. Walden, 1993.
Bruns A. Fourier-, Hilbert- and wavelet-based signal analysis: are they really different approaches? J Neurosci Methods. 2004 Aug 30;137(2):321-32.
A must read on the use of filters has recently been published by Alain de Chéveigné and Israel Nelken in Neuron: Filters: when, why and how (not) to use them.
Another good paper about filtering has been written by Andreas Widmann and colleagues: Digital filter design for electrophysiological data—A practical approach.
The following paper illustrates several problems associated with the lack of robustness and gives recommendations: Rousselet, G.A. & Pernet, C.R. (2012) Improving standards in brain-behavior correlation analyses. Frontiers in human neuroscience, 6, 119.
The paper How to get statistically significant effects in any ERP experiment (and why you shouldn’t) by Steve Luck and Nicholas Gaspelin demonstrates how common methods for quantifying and analyzing ERP effects can lead to very high rates of significant but bogus effects.
The blog post Correlations in neuroscience: are small n, interaction fallacies, lack of illustrations and confidence intervals the norm? by Guillaume Rousselet has some interesting observations and recommendations.
The Meaningfulness of Effect Sizes in Psychological Research: Differences Between Sub-Disciplines and the Impact of Potential Biases by Thomas Schäfer and Marcus A. Schwarz discusses the relevance and challenges of using and reporting effect sizes.
Michel, C.M. et al. EEG source imaging. Clin Neurophysiol, 2004; 115(10):2195-222.
Baillet, S and Mosher, J.C. Electomagnetic Brain Mapping IEEE Signal Processing Magazine, 2001; November:14-30.
The following paper is a review and gentle introduction into beamforming: Hillebrand A, Singh KD, Holliday IE, Furlong PL, Barnes GR. A new approach to neuroimaging with magnetoencephalography. Hum Brain Mapp. 2005 Jun;25(2):199-211.
Schoffelen JM, Gross J. Source connectivity analysis with MEG and EEG. Hum Brain Mapp. 2009 Jun;30(6):1857-65.
Bastos AM, Schoffelen JM. A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls. Front Syst Neurosci. 2016 Jan 8;9:175. doi: 10.3389/fnsys.2015.00175
O’Neill GC, Barratt EL, Hunt BAE, Tewarie PK, Brookes, MJ. Measuring electrophysiological connectivity by power envelope correlation: a technical review on MEG methods. Physics in Medicine and Biology, 2015 60(21), R271–R295.