rb643 / resting_state_eeg Goto Github PK

Looks like the MNE python function isn't quite ironed out 😞

See the error:

/Library/anaconda/envs/py36/lib/python3.6/site-packages/mne/connectivity/spectral.py in spectral_connectivity(data, method, indices, sfreq, mode, fmin, fmax, fskip, faverage, tmin, tmax, mt_bandwidth, mt_adaptive, mt_low_bias, cwt_frequencies, cwt_n_cycles, block_size, n_jobs, verbose)
    877     # compute final connectivity scores
    878     con = list()
--> 879     for method, n_args in zip(con_methods, n_comp_args):
    880         # future estimators will need to be handled here
    881         if n_args == 3:

UnboundLocalError: local variable 'con_methods' referenced before assignment

Might be useful to store the connectivity matrices in a numpy array and write them to a csv file for later use. This can then also be used to check if a subject has already been pre-processed.

I added this a little bit but need to dig into the output structure from the connectivity analysis a bit further... #

Ran into troubles trying to install igraph for python so have converted back to doing it in R :) Started with some examples, but it should be quit easy to start adding graph metrics to the same loop.

One idea I had (and something I've been wanting to try for a while) is using coconut instead of normal python. Coconut is a kind of "extended" python with some extra features. So all code that runs in python will run in coconut as well, but it also has pipe operators (like %>% in R and dplyr):

4 |> maths.sqrt() |> print

is equivalent to:

print(maths.sqrt(4))

Since we're making a pipeline, having a pipe operator could be cool 😃

Currently getting this issue:

RuntimeError: One PCA component captures most of the explained variance, your threshold results in 0 components. You should select a higher value.

Not sure what is going on there - the threshold is set to 0.99, i.e. 99% of variance. So it's suggesting that one PCA component covers 0.99 of variance...

I think I accidentally caused a clash between some folder names, calling it data rather than Data

D:

For some reason the first reading step now gives an error when reading in all bdf files...

ImportError Traceback (most recent call last)
in ()
----> 1 from data import raws, events, n
2
3 for raw in raws():
4 print(raw.info['subject_info'])

ImportError: cannot import name raws

Thanks for sharing the tool, it is beneficial for the starter.
Could you please continue to share the statistical and visualization scripts?
After running all the scripts, I am looking forward to creating the results figure just like the example figures.

do you think we can add some visualization after creating the connectivity matrices, just to check the network?

http://martinos.org/mne/stable/generated/mne.viz.plot_connectivity_circle.html

this should be on the to-do list for later, but we should probably add some functionality to run the pre-processing on single subject and append them to the existing frame, rather than rerun it each time a new dataset is added :)

It would be nice to be able to merge the minimal spanning tree of a graph with the thresholded version so that the final graph has a density above a certain percentage of the mst.

At the moment the union function seems to be able to combine graphs but not attributes. So the weights of each edge are stored as new seperate attributes.

For some strange reason simply adding up those attributes only gives NA's...