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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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