Note
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High Gamma Filter¶
Below is a code sample for extracting high gamma power from a raw data file
from ieeg.navigate import channel_outlier_marker, trial_ieeg
from ieeg.io import raw_from_layout
from ieeg.calc.scaling import rescale
from ieeg.timefreq.utils import crop_pad
from ieeg.timefreq import gamma
from bids import BIDSLayout
import mne
Load Data¶
bids_root = mne.datasets.epilepsy_ecog.data_path()
layout = BIDSLayout(bids_root)
raw = raw_from_layout(layout,
subject="pt1",
preload=True,
extension=".vhdr")
Extracting parameters from /home/docs/mne_data/MNE-epilepsy-ecog-data/sub-pt1/ses-presurgery/ieeg/sub-pt1_ses-presurgery_task-ictal_ieeg.vhdr...
Setting channel info structure...
Reading events from /home/docs/mne_data/MNE-epilepsy-ecog-data/sub-pt1/ses-presurgery/ieeg/sub-pt1_ses-presurgery_task-ictal_events.tsv.
Reading channel info from /home/docs/mne_data/MNE-epilepsy-ecog-data/sub-pt1/ses-presurgery/ieeg/sub-pt1_ses-presurgery_task-ictal_channels.tsv.
Reading electrode coords from /home/docs/mne_data/MNE-epilepsy-ecog-data/sub-pt1/ses-presurgery/ieeg/sub-pt1_ses-presurgery_space-fsaverage_electrodes.tsv.
/home/docs/checkouts/readthedocs.org/user_builds/ieeg-pipelines/checkouts/latest/ieeg/io.py:283: RuntimeWarning: DigMontage is only a subset of info. There are 3 channel positions not present in the DigMontage. The channels missing from the montage are:
['RQ1', 'RQ2', 'N/A'].
Consider using inst.rename_channels to match the montage nomenclature, or inst.set_channel_types if these are not EEG channels, or use the on_missing parameter if the channel positions are allowed to be unknown in your analyses.
whole_raw = read_raw_bids(bids_path=BIDS_path, verbose=verbose)
/home/docs/checkouts/readthedocs.org/user_builds/ieeg-pipelines/checkouts/latest/ieeg/io.py:283: RuntimeWarning: Unable to map the following column(s) to to MNE:
outcome: S
engel_score: 1.0
ilae_score: 2.0
date_follow_up: n/a
ethnicity: 0.0
years_follow_up: 3.0
site: NIH
clinical_complexity: 1.0
whole_raw = read_raw_bids(bids_path=BIDS_path, verbose=verbose)
Reading 0 ... 269079 = 0.000 ... 269.079 secs...
Preprocessing¶
# Mark channel outliers as bad
channel_outlier_marker(raw, 5)
# Exclude bad channels
raw.drop_channels(raw.info['bads'])
good = raw.copy()
good.load_data()
# Remove intermediates for memory efficiency
del raw
# CAR (common average reference)
good.set_eeg_reference()
outlier round 1 channels: ['N/A']
outlier round 2 channels: ['N/A', 'RQ2']
outlier round 3 channels: ['N/A', 'RQ2', 'AST2']
outlier round 4 channels: ['N/A', 'RQ2', 'AST2', 'AD3']
outlier round 5 channels: ['N/A', 'RQ2', 'AST2', 'AD3', 'PD4']
outlier round 6 channels: ['N/A', 'RQ2', 'AST2', 'AD3', 'PD4', 'G32']
ECoG channel type selected for re-referencing
Applying average reference.
Applying a custom ('ECoG',) reference.
High Gamma Filter¶
Extract the epochs of interest
ev1 = trial_ieeg(good,
event="AD1-4, ATT1,2",
times=(-1, 2),
preload=True)
base = trial_ieeg(good,
event="onset",
times=(-1, 0.5),
preload=True)
# extract high gamma power
gamma.extract(ev1,
copy=False,
n_jobs=1)
gamma.extract(base,
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")
Used Annotations descriptions: [np.str_('AD1-4, ATT1,2'), np.str_('AST1,3'), np.str_('G16'), np.str_('PD'), np.str_('SLT1-3'), np.str_('offset'), np.str_('onset')]
Not setting metadata
1 matching events found
No baseline correction applied
0 projection items activated
Using data from preloaded Raw for 1 events and 3001 original time points ...
0 bad epochs dropped
Used Annotations descriptions: [np.str_('AD1-4, ATT1,2'), np.str_('AST1,3'), np.str_('G16'), np.str_('PD'), np.str_('SLT1-3'), np.str_('offset'), np.str_('onset')]
Not setting metadata
1 matching events found
No baseline correction applied
0 projection items activated
Using data from preloaded Raw for 1 events and 1501 original time points ...
0 bad epochs dropped
0%| | 0/1 [00:00<?, ?it/s]
100%|██████████| 1/1 [00:00<00:00, 18.16it/s]
0%| | 0/1 [00:00<?, ?it/s]
100%|██████████| 1/1 [00:00<00:00, 40.17it/s]
Baseline Normalization¶
Z-score normalize ev1 in-place to save memory
Applying baseline correction (mode: zscore)
Plotting¶
<Figure size 640x300 with 2 Axes>
Total running time of the script: (0 minutes 3.706 seconds)
Estimated memory usage: 1320 MB
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