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| The MEG Master Class tutorial series combines theoretical discussions with practical sessions on many aspects of MEG data analysis using SPM. Working from the [javascript:void(0);/*1240483827788*/ SPM example pathway] each processing step is split into a small two hour practical. See links below for individual session notes and worksheets. | The MEG Master Class tutorial series combines theoretical discussions with practical sessions on many aspects of MEG data analysis using SPM. Working from the SpmDemo each processing step is split into a small two hour practical. See links below for individual session notes and worksheets. |
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| Please note that these sessions do not cover max filtering of the data set - this can be done as shown at the start of the [javascript:void(0);/*1240484225540*/ SPM example pathway] script and more details on the processing options are available [javascript:void(0);/*1240484257532*/ here]. | Please note that these sessions do not cover max filtering of the data set - this can be done as shown at the start of the SpmDemo script and more details on the processing options are available on the [[maxpreproc|maxpreproc ]]page. |
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| * Importing raw FIF data Into Matlab. * Spliting in Mags, Grads and EEG data sets. * Reading the Trigger line and extracting the triggers and response codes - see also IdentifyingEventsWithTriggers * Epoching. * Resynching to a new starting point. * Filtering. * Artefact rejection. * Contrasts. * Grand averaging. * Inspecting the results - topographies, line plots, meg_viewdata. === Session 2. 3D Sensor SPM's === 3D sensor SPMs can be used for a sensor level analysis and as a guide to establish critical time windows to analyse further. |
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| * Generating the files. * Computing the anova. * Interpreting the results. Download the worksheet to generate the results: [[attachment:Session2.doc]] |
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| Download the worksheet to interpret the results: [[attachment:Session2A.doc]] | |
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| Download the matlab script: [[attachment:Session2-script.m]] === Session 3. Catch up session - THEORETICAL === * When to take the RMS. * How are bad channels dealt with. * Things to be careful about when you have very uneven trial numbers across conditions (e.g. in an MMN expt). === Session 4. Using ICA for artefact rejection === * How and why to use ICA. * Implementing ICA to extract eye blinks, eye movements and cardiac signals. Download the matlab script: [[attachment:Session4-script.m]] === Session 5. Source reconstuction - THEORETICAL === * Forward model, choice of inverse solution and associated parameters. |
MEG MasterClass
The MEG Master Class tutorial series combines theoretical discussions with practical sessions on many aspects of MEG data analysis using SPM. Working from the SpmDemo each processing step is split into a small two hour practical. See links below for individual session notes and worksheets.
Please note that these sessions do not cover max filtering of the data set - this can be done as shown at the start of the SpmDemo script and more details on the processing options are available on the maxpreprocpage.
Session 1. Pre-processing
- Importing raw FIF data Into Matlab.
- Spliting in Mags, Grads and EEG data sets.
Reading the Trigger line and extracting the triggers and response codes - see also IdentifyingEventsWithTriggers
- Epoching.
- Resynching to a new starting point.
- Filtering.
- Artefact rejection.
- Contrasts.
- Grand averaging.
- Inspecting the results - topographies, line plots, meg_viewdata.
Session 2. 3D Sensor SPM's
3D sensor SPMs can be used for a sensor level analysis and as a guide to establish critical time windows to analyse further.
- Generating the files.
- Computing the anova.
- Interpreting the results.
Download the worksheet to generate the results: Session2.doc
Download the worksheet to interpret the results: Session2A.doc
Download the matlab script: Session2-script.m
Session 3. Catch up session - THEORETICAL
- When to take the RMS.
- How are bad channels dealt with.
- Things to be careful about when you have very uneven trial numbers across conditions (e.g. in an MMN expt).
Session 4. Using ICA for artefact rejection
- How and why to use ICA.
- Implementing ICA to extract eye blinks, eye movements and cardiac signals.
Download the matlab script: Session4-script.m
Session 5. Source reconstuction - THEORETICAL
- Forward model, choice of inverse solution and associated parameters.