- MNE version: 0.24.0
- operating system: Windows 10
Hi,
I want to show the ECoG recording (Three channels from three animals) I have taken with bipolar electrodes as a specific band frequency as in the link time_frequency_global_field_power . The data does not have epochs and events. But I applied mne.make_fixed_length_events / epoch codes. I converted the raw data to the fif file and tried to apply the codes as in the link/below.
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
# let's explore some frequency bands
iter_freqs = [
('Theta', 4, 7),
('Alpha', 8, 12),
('Beta', 13, 25),
('Gamma', 30, 45)]
# set epoching parameters
event_id, tmin, tmax = 1, -1., 3.
baseline = None
# get the header to extract events
raw = mne.io.read_raw_fif('C:\\Users\Mehmet\Desktop\sczf.fif')
events = mne.make_fixed_length_events(raw)
frequency_map = list()
for band, fmin, fmax in iter_freqs:
# (re)load the data to save memory
raw = mne.io.read_raw_fif('C:\\Users\Mehmet\Desktop\sczf.fif')
raw.pick_types(meg='grad', eog=False, ecog=True) # we just look at gradiometers
raw.load_data()
# bandpass filter
raw.filter(fmin, fmax, n_jobs=1, # use more jobs to speed up.
l_trans_bandwidth=1, # make sure filter params are the same
h_trans_bandwidth=1) # in each band and skip "auto" option.
# epoch
epochs = mne.make_fixed_length_epochs (raw)
# remove evoked response
epochs.subtract_evoked()
# get analytic signal (envelope)
epochs.apply_hilbert(envelope=True)
frequency_map.append(((band, fmin, fmax), epochs.average()))
del epochs
del raw
# Helper function for plotting spread
def stat_fun(x):
"""Return sum of squares."""
return np.sum(x ** 2, axis=0)
# Plot
fig, axes = plt.subplots(4, 1, figsize=(10, 7), sharex=True, sharey=True)
colors = plt.get_cmap('winter_r')(np.linspace(0, 1, 4))
for ((freq_name, fmin, fmax), average), color, ax in zip(
frequency_map, colors, axes.ravel()[::-1]):
times = average.times * 1e3
gfp = np.sum(average.data ** 2, axis=0)
gfp = mne.baseline.rescale(gfp, times, baseline=(None, 0))
ax.plot(times, gfp, label=freq_name, color=color, linewidth=2.5)
ax.axhline(0, linestyle='--', color='grey', linewidth=2)
ci_low, ci_up = bootstrap_confidence_interval(average.data, random_state=0,
stat_fun=stat_fun)
ci_low = rescale(ci_low, average.times, baseline=(None, 0))
ci_up = rescale(ci_up, average.times, baseline=(None, 0))
ax.fill_between(times, gfp + ci_up, gfp - ci_low, color=color, alpha=0.3)
ax.grid(True)
ax.set_ylabel('GFP')
ax.annotate('%s (%d-%dHz)' % (freq_name, fmin, fmax),
xy=(0.95, 0.8),
horizontalalignment='right',
xycoords='axes fraction')
ax.set_xlim(-1000, 3000)
axes.ravel()[-1].set_xlabel('Time [ms]')
However, I got the error “No channels match the selection”. I ran the codes step by step and gives this error in the following line of code.
for band, fmin, fmax in iter_freqs:
# (re)load the data to save memory
raw = mne.io.read_raw_fif('C:\\Users\Mehmet\Desktop\sczf.fif')
**raw.pick_types(meg='grad', eog=False, ecog=True)** # we just look at gradiometers
raw.load_data()
I would be glad if you can help.
