Thanks Niels - I'm sure people will find this helpful - apologies to all that we haven't yet got as far as releasing our melview python viewer with similar functionality.
Anyway - we have now released the data that was fed into FIX to train it - to create the trained-weights files that are supplied with FIX. These data are primarily the MELODIC ICA outputs (ICA component spatial maps, timecourses and temporal power spectra) that are fed into FIX, which it then classifies into "good vs bad" ICA components. We also supply the hand-classifications. The intention is primarily that researchers may want to look at these files in order to see in more detail how we did the hand classifications.
To get these, see:
http://www.fmrib.ox.ac.uk/analysis/FIX-training/
Cheers
On 19 Sep 2014, at 16:31, Niels Janssen <[log in to unmask]> wrote:
> I have written a small python program for manually classifying ICs
> extracted with melodic into 'good' and 'bad' components. The program
> displays the ICs with timeseries and power information, and simply loops
> through the images in the filtered_func_data.ica/report/ folder. The
> user presses 'r' (remove) and 'k' (keep) for each component and the
> program writes two text files that contain the 'bad' and 'good'
> components. These text files can then be used for further purposes such
> as fsl_regfilt and fsl fix.
>
> I think the program is useful because it is much faster than looking at
> ICs in the html files.
>
> The program with further instructions can be found here:
> https://github.com/iamnielsjanssen/display_melodics
>
> Thank you very much
>
> Niels Janssen
>
>
> --
> Niels Janssen
> Cognitive Neuroscience and Psycholinguistics Laboratory
> University of La Laguna
> Tenerife, Spain
> http://www.neurocog.ull.es/en
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Stephen M. Smith, Professor of Biomedical Engineering
Associate Director, Oxford University FMRIB Centre
FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK
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