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| Your life as a neuroimager will be much easier if you have a good grasp on Linux and Matlab. | Your life as a neuroimager will be much easier if you have a good grasp on Linux (Unix) and Matlab. In general, I would recommend "learning by doing", but the following links will get you on your way, and will serve as useful references when you get stuck. |
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| Matlab: | '''Matlab: ''' |
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| You can look at [http://www.mathworks.com/academia/student_center/tutorials/launchpad.html tutorials and demos] offered by Matlab itself. This site also contains links to Matlab tutorials at some universities. You may want to start with the [http://www.mathworks.com/access/helpdesk/help/pdf_doc/matlab/getstart.pdf Getting Started] section. The short introduction to [http://imaging.mrc-cbu.cam.ac.uk/imaging/LearningMatlab Matlab for psychologists] from the University of York is also useful. |
You can look at [http://www.mathworks.com/academia/student_center/tutorials/launchpad.html tutorials and demos] offered by Matlab itself. This site also contains links to Matlab tutorials at some universities. You may want to start with the [http://www.mathworks.com/access/helpdesk/help/pdf_doc/matlab/getstart.pdf Getting Started] section. The short introduction to [http://imaging.mrc-cbu.cam.ac.uk/imaging/LearningMatlab Matlab for psychologists] from the University of York is also useful. |
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| '''Linux (Unix): ''' | |
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| This is a systemtic and well-illustrated [http://www.ee.surrey.ac.uk/Teaching/Unix/ introduction to linux]. '''Other sofware:''' Most software packages will offer tutorials and demos. They will familiarise you with basic analysis principles, visualisation options, and give you an overview of what's available to you. It could make sense to do the tutorials of software packages even if you are not planning to use them routinely. For example, MNE includes an example data set (tutorial in the manual), And finally another piece of advice: Talk to people! Ask questions! Try things out! And keep trying... |
Analysing data
There are several options for analysis of MEG data from our machine available at the CBU:
NeuromagSoftware that comes with the machine (instructions on AccessingNeuromagSoftware at CBU)
CommercialPackages (CURRY, BESA, ASA, EMSE), for which we have licences (look here for a possible [http://imaging.mrc-cbu.cam.ac.uk/meg/AnalysisPath analysis route])
FreewarePackages, such as Fieldtrip, [http://imaging.mrc-cbu.cam.ac.uk/meg/DansPage MNE], and [http://imaging.mrc-cbu.cam.ac.uk/meg/SpmAnalysis SPM].
You can get some general feeling about the analysis pathway from the [attachment:MEGAnalysisLectureJan07CBU.pdf CBU introductory lecture into MEG analysis] (the slides do not include the live software demos given at the lecture).
Whatever your analysis path is, you will most likely start by applying [:Maxfilter:Max Filter] on your raw data.
For some notes on identifying events from trigger channels, see IdentifyingEventsWithTriggers.
If all this is new to you...
You may want to start with a basic [http://www.mrc-cbu.cam.ac.uk/research/eeg/eeg_intro.html introduction to EEG and MEG analysis].
Your life as a neuroimager will be much easier if you have a good grasp on Linux (Unix) and Matlab. In general, I would recommend "learning by doing", but the following links will get you on your way, and will serve as useful references when you get stuck.
Matlab:
You can look at [http://www.mathworks.com/academia/student_center/tutorials/launchpad.html tutorials and demos] offered by Matlab itself. This site also contains links to Matlab tutorials at some universities. You may want to start with the [http://www.mathworks.com/access/helpdesk/help/pdf_doc/matlab/getstart.pdf Getting Started] section. The short introduction to [http://imaging.mrc-cbu.cam.ac.uk/imaging/LearningMatlab Matlab for psychologists] from the University of York is also useful.
Linux (Unix):
This is a systemtic and well-illustrated [http://www.ee.surrey.ac.uk/Teaching/Unix/ introduction to linux].
Other sofware:
Most software packages will offer tutorials and demos. They will familiarise you with basic analysis principles, visualisation options, and give you an overview of what's available to you. It could make sense to do the tutorials of software packages even if you are not planning to use them routinely. For example, MNE includes an example data set (tutorial in the manual),
And finally another piece of advice: Talk to people! Ask questions! Try things out! And keep trying...