Note
Go to the end to download the full example code.
PCA-LDA Decoding¶
Takes high gamma filtered data with event labels from multiple subjects and performs joint pca decoding
from ieeg.decoding.models import PcaLdaClassification
from ieeg.arrays.label import LabeledArray
from sklearn.metrics import ConfusionMatrixDisplay
from ieeg.decoding.decode import Decoder, classes_from_labels
from ieeg.navigate import channel_outlier_marker, trial_ieeg, outliers_to_nan
from ieeg.calc.scaling import rescale
from ieeg.timefreq.utils import crop_pad
from ieeg.timefreq import gamma
from ieeg.mt_filter import line_filter
import mne
import numpy as np
import matplotlib.pyplot as plt
WARNING: statsmodels is not installed. You will be unable to use the Naive Bayes Decoder
WARNING: sklearnex is not installed. You will be unable to use thePCA decoder acceleration
WARNING: Xgboost package is not installed. You will be unable touse the xgboost decoder
WARNING: Keras package is not installed. You will be unable to useall neural net decoders
Load Data¶
misc_path = mne.datasets.misc.data_path()
raw = mne.io.read_raw(misc_path / 'seeg' / 'sample_seeg_ieeg.fif',
preload=True)
Opening raw data file /home/docs/mne_data/MNE-misc-data/seeg/sample_seeg_ieeg.fif...
Range : 1310640 ... 1370605 = 1311.411 ... 1371.411 secs
Ready.
Reading 0 ... 59965 = 0.000 ... 60.000 secs...
Filter the data to remove line noise¶
line_filter(raw, mt_bandwidth=10., n_jobs=1, copy=False, verbose=10,
filter_length='700ms', freqs=[60], notch_widths=20)
# line_filter(raw, mt_bandwidth=10., n_jobs=-1, copy=False, verbose=10,
# filter_length='7s', freqs=[60, 120, 180, 240],
# notch_widths=20)
raw.plot()
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<mne_qt_browser._pg_figure.MNEQtBrowser object at 0x74b0bfd704d0>
Preprocessing¶
# Mark channel outliers as bad
channel_outlier_marker(raw, 3, 2)
# Exclude bad channels, then load the good channels into memory
raw.drop_channels(raw.info['bads'])
good = raw.copy()
good.load_data()
# CAR (common average reference)
good.set_eeg_reference()
del raw
outlier round 1 channels: ['LAMY 7']
outlier round 1 channels: ['LAMY 7', 'LPHG 6']
outlier round 1 channels: ['LAMY 7', 'LPHG 6', 'LBRI 3']
outlier round 1 channels: ['LAMY 7', 'LPHG 6', 'LBRI 3', 'RAHP 3']
outlier round 2 channels: ['LAMY 7', 'LPHG 6', 'LBRI 3', 'RAHP 3', 'LENT 3']
outlier round 2 channels: ['LAMY 7', 'LPHG 6', 'LBRI 3', 'RAHP 3', 'LENT 3', 'LPIT 5']
sEEG channel type selected for re-referencing
Applying average reference.
Applying a custom ('sEEG',) reference.
High Gamma Filter¶
# extract the epochs of interest
ev1 = trial_ieeg(good, ["Fixation", "ISI Onset", "Go Cue", "Response"],
(-0.6, 0.7), preload=True)
base = trial_ieeg(good, "Fixation", (-1, 0.5), preload=True)
outliers_to_nan(ev1, outliers=10)
outliers_to_nan(base, outliers=10)
# extract high gamma power
gamma.extract(ev1, passband=(70, 150), copy=False, n_jobs=1)
gamma.extract(base, passband=(70, 150), copy=False, n_jobs=1)
# trim 0.5 seconds on the beginning and end of the data (edge artifacts)
crop_pad(ev1, "500ms")
crop_pad(base, "500ms")
# ev1["Response"]._data += np.sin(np.linspace(0, 2*np.pi, 401)) * 10
Used Annotations descriptions: [np.str_('Fixation'), np.str_('Go Cue'), np.str_('ISI Onset'), np.str_('Response')]
Not setting metadata
32 matching events found
No baseline correction applied
0 projection items activated
Using data from preloaded Raw for 32 events and 1301 original time points ...
2 bad epochs dropped
Used Annotations descriptions: [np.str_('Fixation'), np.str_('Go Cue'), np.str_('ISI Onset'), np.str_('Response')]
Not setting metadata
8 matching events found
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0 projection items activated
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reformat data for decoding¶
Applying baseline correction (mode: zscore)
Create a Decoder¶
cats, labels = classes_from_labels(arr.labels[0])
decoder = Decoder(cats, explained_variance=0.80,
n_splits=5, n_repeats=100)
cm = decoder.cv_cm(arr.__array__().swapaxes(0, 1), labels,
normalize='true', oversample=True)
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fig, ax = plt.subplots()
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=cats.keys())
disp.plot(ax=ax)
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x74b0ab4e7d70>
Total running time of the script: (1 minutes 5.643 seconds)
Estimated memory usage: 724 MB
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