101 lines
4.4 KiB
Python
101 lines
4.4 KiB
Python
import mne
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib import cm
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from matplotlib.colors import LogNorm
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from utils.file_utils import load_preprocessed_data, get_keys_for_events
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def plot_grand_average(dataset):
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"""
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Plot the grand average ERPs
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:param dataset: the datset for which the grand average is computed
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"""
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evtss = [('face', 'intact'), ('face', 'scrambled'), ('car', 'intact'), ('car', 'scrambled')]
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chs = ['P7', 'PO7', 'P8', 'PO8']
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for ch in chs:
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fi = []
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fs = []
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ci = []
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cs = []
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for i in range(1, 41):
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subj = "0" + str(i)
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if len(str(i)) == 1:
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subj = "0" + subj
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# Load preprocessed .fif data files
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raw = load_preprocessed_data(subj, dataset)
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# Epoch the data
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for ev in evtss:
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wanted_keys = get_keys_for_events(ev[0], ev[1])
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events, events_dict = mne.events_from_annotations(raw)
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events_dict_key = dict((k, events_dict[k]) for k in wanted_keys if k in events_dict)
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epochs = mne.Epochs(raw, events, events_dict_key, tmin=-0.1, tmax=1, reject_by_annotation=True,
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picks=[ch])
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# Get the N170 peak
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# First construct a data frame
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if ev[0] == 'face' and ev[1] == 'intact':
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fi.append(epochs.average(picks=[ch]))
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elif ev[0] == 'face' and ev[1] == 'scrambled':
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fs.append(epochs.average(picks=[ch]))
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elif ev[0] == 'car' and ev[1] == 'intact':
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ci.append(epochs.average(picks=[ch]))
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elif ev[0] == 'car' and ev[1] == 'scrambled':
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cs.append(epochs.average(picks=[ch]))
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ga_fi = mne.grand_average(fi)
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ga_ci = mne.grand_average(ci)
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ga_fs = mne.grand_average(fs)
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ga_cs = mne.grand_average(cs)
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ga_fi.comment = 'Face Intact'
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ga_ci.comment = 'Car Intact'
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ga_fs.comment = 'Face Scrambled'
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ga_cs.comment = 'Car Scrambled'
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mne.viz.plot_compare_evokeds([ga_fi, ga_ci, ga_fs, ga_cs], picks=ch, colors=['blue', 'red', 'blue', 'red'],
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linestyles=['solid', 'solid', 'dotted', 'dotted'])
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def plot_tf_cluster(F, clusters, cluster_p_values, freqs, times):
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"""
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Plot the F-Statistic values of permutation clusters with p-values <= 0.05 in color and > 0.05 in grey.
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Currently only works well for the linear scaling. For the logarithmic scaling a different x-axis has to be chosen
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:param F: F-Statistics of the permutation clusters
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:param clusters: all permutation clusters
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:param cluster_p_values: p-values of the clusters
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:param freqs: frequency domain
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:param times: time domain
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"""
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good_c = np.nan * np.ones_like(F)
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for clu, p_val in zip(clusters, cluster_p_values):
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if p_val <= 0.05:
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good_c[clu] = F[clu]
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bbox = [times[0], times[-1], freqs[0], freqs[-1]]
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plt.imshow(F, aspect='auto', origin='lower', cmap=cm.gray, extent=bbox, interpolation='None')
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a = plt.imshow(good_c, cmap=cm.RdBu_r, aspect='auto', origin='lower', extent=bbox, interpolation='None')
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plt.colorbar(a)
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plt.xlabel('Time (s)')
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plt.ylabel('Frequency (Hz)')
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plt.show()
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def plot_oscillation_bands(condition):
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"""
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Plot the oscillation bands for a given condition in the time from 130ms to 200ms
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:param condition: the condition to plot the oscillation bands for
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"""
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fig, axis = plt.subplots(1, 5, figsize=(25, 5))
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condition.plot_topomap(baseline=(-0.2, 0), fmin=0, fmax=4, title='Delta', axes=axis[0], show=False, vmin=0,
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vmax=1.5, tmin=0.13, tmax=0.2)
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condition.plot_topomap(baseline=(-0.2, 0), fmin=4, fmax=8, title='Theta', axes=axis[1], show=False, vmin=0,
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vmax=0.7, tmin=0.13, tmax=0.2)
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condition.plot_topomap(baseline=(-0.2, 0), fmin=8, fmax=12, title='Alpha', axes=axis[2], show=False, vmin=-0.25,
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vmax=0.2, tmin=0.13, tmax=0.2)
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condition.plot_topomap(baseline=(-0.2, 0), fmin=13, fmax=30, title='Beta', axes=axis[3], show=False, vmin=-0.21,
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vmax=0.2, tmin=0.13, tmax=0.2)
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condition.plot_topomap(baseline=(-0.2, 0), fmin=30, fmax=45, title='Gamma', axes=axis[4], vmin=-0.05, vmax=0.2,
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tmin=0.13,
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tmax=0.2)
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