[Mne_analysis] 1 Samp Cluster
Geller, Jason
jason-geller at uiowa.edu
Tue Oct 29 16:55:36 EDT 2019
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When running 1 Samp Cluster on the difference between two conditions, the H1 Stat always comes back as min = 0 and max = 0 with no clusters found. Now, it is possible that there is no difference between the conditions, but given the fact that min and max are both 0 has me concerned I did something wrong. Any insight?
> On Oct 29, 2019, at 11:00 AM, mne_analysis-request at nmr.mgh.harvard.edu wrote:
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> 1. Re: Computing Connectivity 1Samp Cluster (Geller, Jason)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Mon, 28 Oct 2019 20:47:51 +0000
> From: "Geller, Jason" <jason-geller at uiowa.edu>
> Subject: Re: [Mne_analysis] Computing Connectivity 1Samp Cluster
> To: "mne_analysis at nmr.mgh.harvard.edu"
> <mne_analysis at nmr.mgh.harvard.edu>
> Message-ID:
> <DM6PR04MB380127E0FB929C10D448159684660 at DM6PR04MB3801.namprd04.prod.outlook.com>
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> Hello,
>
> I am trying to compute the connectivity matrix after having morphed all my Ss to fsaverage so I can perform a cluster permutation test. I keep getting this error:
>
> ValueError: connectivity (len 8196) must be of the correct size, i.e. be equal to or evenly divide the number of tests (50370156).
>
> If connectivity was computed for a source space, try using the fwd["src"] or inv["src"] as some original source space vertices can be excluded during forward computation
>
> This is the function I am using to get stcs for each Subject and Condition;
>
> def morph_data_to_fsaverage(subject, inverse, path6, stc, save_dir, subjects_dir, method, overwrite):
> inverse_operator = read_inverse_operator(inverse + subject + '-inv.fif') # each subject inv file
> src=mne.read_source_spaces(path6 +'fsaverage-oct-6-src.fif') #fsaverage src
>
> stcs = mne.read_source_estimate(stc + subject + '_' + 'NS' + '_' + 'dSPM' + '-lh.stc') # only left hemisphere
>
>
> subject_to = 'fsaverage'
> stc_morph_name = subject + '_' + 'VO6' + '_' + method + '_morph'
> stc_morph_path = save_dir + stc_morph_name
>
>
> src=inverse_operator['src']
> stcs_morph = mne.compute_source_morph(src,subject_to,
> subjects_dir=subjects_dir).apply(stcs)
>
>
> stcs_morph.save(stc_morph_path)
>
>
>
>
>
> ________________________________
> From: mne_analysis-bounces at nmr.mgh.harvard.edu <mne_analysis-bounces at nmr.mgh.harvard.edu> on behalf of mne_analysis-request at nmr.mgh.harvard.edu <mne_analysis-request at nmr.mgh.harvard.edu>
> Sent: Sunday, October 27, 2019 11:00 AM
> To: mne_analysis at nmr.mgh.harvard.edu <mne_analysis at nmr.mgh.harvard.edu>
> Subject: [External] Mne_analysis Digest, Vol 141, Issue 33
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> 1. Re: INCORRECT VALUES PRODUCED WHEN EPOCHING A CONTINOUS
> DISCRETE SIGNAL USING MNE EPOCH FUNCTION (Dan McCloy)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Sat, 26 Oct 2019 09:58:29 -0700
> From: Dan McCloy <dan.mccloy at gmail.com>
> Subject: Re: [Mne_analysis] INCORRECT VALUES PRODUCED WHEN EPOCHING A
> CONTINOUS DISCRETE SIGNAL USING MNE EPOCH FUNCTION
> To: Discussion and support forum for the users of MNE Software
> <mne_analysis at nmr.mgh.harvard.edu>
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> mne.Epochs defaults to tmin=-0.2 and tmax=0.5. So you are not getting
> 20-second long epochs, you are getting 700ms epochs. You need to set, for
> example, tmin=0 and tmax=20, or tmin=-5 and tmax=15, etc.
>
> On Sat, Oct 26, 2019 at 6:47 AM RODNEY PETRUS BALANDONG <
> rodney.petrus_g03291 at utp.edu.my> wrote:
>
>> External Email - Use Caution
>>
>> Dear All,
>>
>>
>>
>> The idea was to epoch the continuous EEG data of 386.936 s long into non
>> overlapping epoch window, of size 20 s. With a sampling frequency 250 Hz,
>> theoretically each epochs should contain 5000 data points per epoch.
>>
>>
>>
>> To achieve the objective, the following code was utilised,
>>
>>
>>
>> *epochs = mne.Epochs(raw, events=events, event_id=event_id,
>> baseline=None, verbose=True)*
>>
>> *MneApproach=epochs.to_data_frame()*
>>
>>
>>
>>
>>
>> To confirm whether the value return from the mne.Epoch was correct or
>> not, I had created a script that can performed the epoching manually. The
>> output from the script has been validated visually and was working as
>> intended.
>>
>> However, I noticed there were different between the script output and the
>> value from dataframe MneApproach. Apart from different values, the
>> MneApproach contained only 176 datasets per epoch.
>>
>>
>>
>> May I know what did I do wrong while inputting the mne.Epoch function.
>>
>>
>>
>> The above problem can be reproduced from the following ipynb
>> <https://colab.research.google.com/github/balandongiv/Downsample/blob/master/helpMne.ipynb>
>> via Google Colab
>>
>>
>>
>>
>>
>> Really appreciate for any feedback and help.
>>
>>
>>
>> Regards
>>
>> Rpb
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