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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