[Mne_analysis] epoch.get_data( ) returns data of shape (n_channel, times)

Alexandre Gramfort alexandre.gramfort at inria.fr
Tue Jan 21 04:11:41 EST 2020
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        External Email - Use Caution        

hi,

it's weird.

can you save your epochs with *epochs.save and share the files with me so I
can look?*

*thx*
*A*

On Mon, Jan 20, 2020 at 11:54 PM Ben Ighoyota Ajenaghughrure <
ighoyota at tlu.ee> wrote:

>         External Email - Use Caution
>
> Hello All,
>
> I am experiencing something strange with my data analysis code that worked
> few weeks ago but currently is failing on new data sets
>
> Using the snippet below, i import a csv file containing eeg data  and
> events. Then i filtered the data, and epoch the data.
> but when i call epoch.get_data(), instead of getting a 3D array of
> n-epochs by n_channel by_ time, i am getting only n_channel by n_times.
> I also see that all epochs are considered bad and dropped.
> If this is the case, how can I avoid this?(dropping all epochs as bad.)
>
> how can i go about resolving this?.
>
> Looking forward to your reply
>
>
> *Code Snippet*
> *#libraries imported*
>
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> *import numpy as npimport mneimport osfrom mne.preprocessing import
> compute_proj_ecg, compute_proj_eogfrom mne_features.feature_extraction
> import FeatureExtractorimport pandas as pdfrom
> mne_features.feature_extraction import extract_featuresfrom
> mne_features.univariate import compute_pow_freq_bandsfrom
> mne_features.utils import power_spectrum*
>
> *# Read the raw data from their respective CSV file as a NumPy array*
>
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> *data = np.loadtxt('eegdata.csv', delimiter=',') #reading the raw eeg
> signaevenfile=np.loadtxt('eegevent.csv', delimiter=',')#reading the eeg
> event file with timestampsevenfile=np.array(evenfile, dtype='int')#ensuring
> that the event file contains only int values and no float# Some information
> about the channelschannames =
> ['Cz','Fz','C3','C4','F3','F4','P7','P8','stim'] ch_types =
> ['eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','stim']# Sampling rate of
> the Nautilus machinesfreq = 250  # Hz for eeg*
>
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> *# Create the info structure needed by MNEinfo =
> mne.create_info(ch_names=channames, sfreq=sfreq, ch_types=ch_types)#specify
> the events in the eeg dataevent_id =1# Finally, create the Raw objectraw =
> mne.io.RawArray(data, info)*
>
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> *picks =mne.pick_types(raw.info <http://raw.info>, meg=False, eeg=True,
> stim=True, eog=False,exclude='bads')#filtering the raw
> signalraw=raw.filter(0.1, 120,
> fir_design='firwin')raw=raw.notch_filter(np.arange(50, 125, 50),
> picks=picks, filter_length='auto',                 phase='zero')# Read
> epochsepochs = mne.Epochs(raw, evenfile, event_id=event_id, tmin=-2,
> tmax=2,  picks=picks)labels = epochs.events[:, -1]data = epochs.get_data()*
>
>
> Best Regards
> A. Ighoyota ben
> Junior Researcher HCI (PhD in-view)
> Tallinn University, Estonia
> School of digital Technologies.
> mobile:+372582 <+372%205832%206393>78794
> skype: ighoyota-ben
> _______________________________________________
> Mne_analysis mailing list
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