[Mne_analysis] epoching mismatch between python and matlab

[Tommaso Fedele]

        External Email - Use Caution        

Dear Alex,

this is actually the answer.
I found out that MNE Python translates the sample info to the real time of
the recording,
which in my case is starting at t = 39 seconds.
So my code works if I build the events array as follows



*events = np.vstack((spevents[0] + raw.first_samp, np.zeros(60),
np.arange(60)+1)).T.astype(np.int <http://np.int>) # array int32 (60,3)
Nevents,3epochs = mne.Epochs(raw, events,  tmin = -0.1, tmax = 0.1, proj =
False, baseline=None, detrend=0, preload=False)     *

where *raw.first_samp* set the correct offset for MNE Python to understand
where I want to point in my continuous data.

Thanks,

Best,
Tommaso



On Wed, Nov 27, 2019 at 4:41 PM Alexandre Gramfort <
alexandre.gramfort at inria.fr> wrote:

>         External Email - Use Caution
>
> hi Tommaso,
>
> unfortunately it's really hard to help without more context and knowledge
> about the data.
>
> Best,
> Alex
>
>
> On Tue, Nov 26, 2019 at 3:36 PM Tommaso Fedele <fedele.tm at gmail.com>
> wrote:
>
>>         External Email - Use Caution
>>
>> Dear MNE people,
>>
>> I got a problem when epoching data in MNE Python using mne.Epochs.
>>
>> I am running some analysis with a Matlab code and I would like to
>> transfer the same analysis to MNE Python.
>>
>> My problem is that if I compare one epoch from Matlab with the output of
>> mne.Epochs in Python they do not correspond.
>>
>> Here below the code and a figure explaining the problem
>>
>> My question is: why
>>
>>   spike1[295, :]  # epoch from matlab data
>>   data[295, (np.arange(index-100,index+100))]   # epoch from data
>> imported in python and manually extracted with a sample reference 'index'
>>
>> do not correspond with
>>
>>   epochs9[0,295,:] # which is the output of mne.Epochs for the very same
>> epoch?
>>
>> They both should refer to the sample 75650, with a pre- and post-trigger
>> of 100 samples.
>>
>> I am not applying any SSP projection or baseline. What am I missing?
>>
>> Thanks in advance for your help,
>>
>> Best,
>> Tommaso
>>
>> %%%%%%%%%%%%%%%%% code
>>
>> import numpy as np
>> import mne
>> import xlrd
>> import scipy.io as sio
>> import pandas as pd
>> from mne.viz import plot_evoked_topo
>> import matplotlib.pyplot as plt
>>
>> spike = sio.loadmat(
>>         'E:/../spike75650.mat'
>>         )# data imported from Matlab as dict spike.
>>          #The field 'spike is an array float64 (404, 201) channels,
>> samples
>>
>>
>> raw = mne.io.read_raw_fif('E:/.../B1C2_ii_run1_raw_tsss_mc_art_corr.fif')
>> #data after tsss
>> excel = xlrd.open_workbook('E:/.../list raw detections B1C2.xlsx')
>> #trigger info from excel file
>>
>> # create an array for mne.Epochs
>> sheet = excel.sheet_by_index(2)
>> sp=list()
>> for i in range(1,61):
>>     sp.append(sheet.cell_value(i,2))
>> spevents     = np.array(sp, ndmin=2, dtype=int)
>> zeros        = np.zeros((1,60), dtype=int)
>> event_id_int = np.ones((1,60), dtype=int)
>> event_id_int[:,:] = np.arange(0,60)
>> event_id_str = list(map(str,np.arange(0,60)))
>> events = np.hstack((spevents.T, zeros.T, event_id_int.T)) # array int32
>> (60,3) Nevents,3
>>
>>                  # one row is 75650 0 9
>>
>> # Create epochs including different events
>> event_id = dict(zip(event_id_str, np.arange(0,60)))
>> epochs   = mne.Epochs(raw, events, event_id, tmin = -0.1, tmax = 0.1,
>> proj = False)
>>
>> # Generate list of evoked objects from conditions names
>> evokeds = [epochs[name].average() for name in np.arange(0,60)]
>>
>>
>> ##################### Figure
>> plt.close('all')
>> plt.figure()
>>
>> ep_id = 9
>> index = spevents[0,ep_id] # trigger sample for continuous data, value
>> 75650
>>
>> # spike from matlab
>> spike1 = spike['spike']
>> plt.plot(spike1[295, :],
>>                        label = ['Matlab: spike from raw data - MEG2613'])
>>
>> # spike from continuous data
>> plt.plot(data[295, (np.arange(index-100,index+100))]-5*10**-11,
>>                             label = ['Python: spike from raw data -
>> MEG2613'])
>>
>>  # spike from MNE epochs
>> epochs9 = epochs[ep_id].get_data()
>> plt.plot(epochs9[0,295,:]-5*2*10**-11,
>>                         label = ['MNE Python: spike from EPOCHED data -
>> MEG2613'])
>>
>> # spike from MNE average
>> evoked9 = epochs[ep_id].average()
>> plt.plot(evoked9.data[295]-5*3*10**-11,
>>                         label = ['MNE Python: spike from averaged data -
>> MEG2613'])
>>
>> plt.legend()
>>
>> # check that the channel labels is the same
>> print(epochs.ch_names[295]), print(raw.ch_names[295])
>>
>> %%%%%%%%%%%%%%%%%%%%%% FIGURE
>>
>> [image: image.png]
>>
>>
>>
>>
>>
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-- 
Tommaso Fedele
Assistant professor, Institute of Cognitive Neuroscience
Higher School of Economics - National Research University
101000, Moscow, 18 Myasnitskaya Ulitsa
Russian Federation
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