Preamble¶

In [1]:

import mne
import numpy as np                 # for multi-dimensional containers
import pandas as pd                # for DataFrames
import plotly.graph_objects as go  # for data visualisation
import plotly.io as pio            # to set shahin plot layout

pio.templates['shahin'] = pio.to_templated(go.Figure().update_layout(yaxis=dict(autorange = "reversed"),margin=dict(t=0,r=0,b=40,l=40))).layout.template
pio.templates.default = 'shahin'
pio.renderers.default = "notebook_connected"

Dataset¶

Download sample data from Mike Cohen.

In [2]:

import warnings
warnings.filterwarnings("ignore")

def load_data(tmin, tmax):
    epochs = mne.io.read_epochs_eeglab('sampleEEGdata.mat').crop(tmin, tmax)
    return epochs

Butterfly Plot of ERP from all sensors (all channels/all trials in time domain)¶

With MNE.Epochs.to_data_frame()¶

In [3]:

epochs = load_data(-0.2,1)
values = epochs.to_data_frame()
values = values.groupby("time").mean()
values.head()

Extracting parameters from sampleEEGdata.mat...
99 matching events found
No baseline correction applied
Not setting metadata
0 projection items activated
Ready.
Converting "time" to "<class 'numpy.int64'>"...

Out[3]:

channel	Fp1	AF7	AF3	F1	F3	F5	F7	FT7	FC5	FC3	...	CP4	CP2	P2	P4	P6	P8	P10	PO8	PO4	O2
time
-199	-0.891870	-0.701918	-1.706986	-1.024161	-0.976764	-1.014164	-1.002770	-1.402991	-1.496777	-1.314428	...	-1.511028	-1.477739	-1.723046	-1.574034	-1.598564	-1.779448	-0.940347	-1.883280	-1.808410	-2.185663
-195	0.233510	0.506950	-0.445840	0.333534	0.195593	0.212527	0.163632	-0.052681	-0.010986	0.129063	...	0.125678	0.130709	0.035723	0.130725	0.134243	0.357075	0.358627	-0.038384	-0.170933	-0.259101
-191	-0.507979	-1.007955	-0.986916	-0.328037	-0.593872	-0.573180	-0.562565	-1.356027	-0.634322	-0.383950	...	0.207923	0.279907	0.127276	0.382046	0.444502	0.647713	0.310199	0.538165	0.387237	0.784565
-188	-0.371972	-0.792418	0.033578	-0.341501	-0.652673	-0.620372	-0.657750	-1.051308	-0.668186	-0.351760	...	-0.173513	-0.230984	-0.433561	-0.238758	-0.352948	-0.364883	0.579337	-0.249470	-0.402957	-0.453058
-184	-0.152894	-0.505432	1.171427	0.201709	-0.251452	-0.286700	-0.341288	-0.404997	-0.500198	-0.006877	...	0.178264	0.226898	-0.141822	0.106690	0.067396	-0.146313	-0.080451	0.000498	0.049574	0.088095

5 rows × 64 columns

In [4]:

fig = go.Figure(layout=dict(xaxis=dict(title='time'),yaxis=dict(title='voltage')))

for ch in epochs.info['ch_names']:
    fig.add_scatter(x=epochs.times, y=values[ch], name=ch)

fig.show()

With MNE.Epochs.get_data()¶

In [5]:

epochs = load_data(-0.2,1)
values = epochs.get_data().mean(axis=0)
values = pd.DataFrame(np.transpose(values), columns=epochs.info['ch_names'])
values.head()

Extracting parameters from sampleEEGdata.mat...
99 matching events found
No baseline correction applied
Not setting metadata
0 projection items activated
Ready.

Out[5]:

	Fp1	AF7	AF3	F1	F3	F5	F7	FT7	FC5	FC3	...	CP4	CP2	P2	P4	P6	P8	P10	PO8	PO4	O2
0	-8.918701e-07	-7.019182e-07	-1.706986e-06	-1.024161e-06	-9.767636e-07	-1.014164e-06	-1.002770e-06	-1.402991e-06	-1.496777e-06	-1.314428e-06	...	-1.511028e-06	-1.477739e-06	-1.723046e-06	-1.574034e-06	-1.598564e-06	-1.779448e-06	-9.403469e-07	-1.883280e-06	-1.808410e-06	-2.185663e-06
1	2.335097e-07	5.069503e-07	-4.458401e-07	3.335338e-07	1.955928e-07	2.125266e-07	1.636316e-07	-5.268077e-08	-1.098647e-08	1.290634e-07	...	1.256775e-07	1.307085e-07	3.572303e-08	1.307252e-07	1.342428e-07	3.570746e-07	3.586267e-07	-3.838408e-08	-1.709330e-07	-2.591015e-07
2	-5.079792e-07	-1.007955e-06	-9.869164e-07	-3.280373e-07	-5.938724e-07	-5.731804e-07	-5.625650e-07	-1.356027e-06	-6.343224e-07	-3.839501e-07	...	2.079225e-07	2.799072e-07	1.272755e-07	3.820456e-07	4.445024e-07	6.477130e-07	3.101987e-07	5.381648e-07	3.872367e-07	7.845649e-07
3	-3.719716e-07	-7.924181e-07	3.357842e-08	-3.415010e-07	-6.526729e-07	-6.203719e-07	-6.577504e-07	-1.051308e-06	-6.681859e-07	-3.517596e-07	...	-1.735130e-07	-2.309843e-07	-4.335609e-07	-2.387584e-07	-3.529476e-07	-3.648834e-07	5.793374e-07	-2.494695e-07	-4.029571e-07	-4.530581e-07
4	-1.528940e-07	-5.054324e-07	1.171427e-06	2.017091e-07	-2.514524e-07	-2.866996e-07	-3.412884e-07	-4.049969e-07	-5.001981e-07	-6.876538e-09	...	1.782636e-07	2.268977e-07	-1.418217e-07	1.066895e-07	6.739592e-08	-1.463131e-07	-8.045127e-08	4.981451e-10	4.957422e-08	8.809529e-08

5 rows × 64 columns

In [6]:

fig = go.Figure(layout=dict(xaxis=dict(title='time')))

for ch in epochs.info['ch_names']:
    fig.add_scatter(x=epochs.times, y=values[ch], name=ch)

fig.show()