from functions import paths
from functions import mne_analysis as mneanalysis
# from mne.time_frequency import tfr_multitaper, tfr_stockwell, tfr_morlet
base_path = 'E:/OneDrive/FGU/iEEG/Data'
participant = 'p136'
scalings = {'seeg': 1e2, 'ecg': 1e2, 'misc': 1e2}
file_paths = paths.prep_unity_alloego_files(base_path, participant)
# To reduce confounding noise artifacts, we used a bipolar referencing scheme
# where by we identified all pairs of immediately adjacent electrodes and
# calculated the voltage difference between both contacts in the pair.
# The location of these new virtual electrodes was taken to be the midpoint
# between the two physical contacts(inter-electrode spacing: 10 mm).
eeg, montage = loader.load_eeg(file_paths, 'bipolar')
pd_events = mneprep.load_preprocessed_events(file_paths)
mne_events, events_mapp = mneprep.pd_to_mne_events(pd_events,
eeg.info['sfreq'])
# Preprocessing
# To further reduce electrical line noise, a band-stop 4th order Butterworth
# filter was applied at 58–62 Hz. To eliminate events contaminated by
# epileptiform activity, we excluded time periods of interest if the
# kurtosis of the voltage trace exceeded a threshold of 563. This in
# the exclusion of an average of 7.1% ± 1.7% of experimental events.
eeg.notch_filter(50)
# Additionally, clinicians at each collaborating hospital identified which
import mne
from functions import mne_prepping as mneprep
from functions import mne_helpers as mnehelp
from functions import mne_loading as loader
from functions import paths
base_path = 'E:/OneDrive/FGU/iEEG/Data'
participant = 'p136'
scalings = {'seeg': 1e2, 'ecg': 1e2, 'misc': 1e2}
file_paths = paths.prep_unity_alloego_files(base_path, participant)
eeg, montage = loader.load_eeg(file_paths, 'perElectrode')
pd_events = mneprep.load_preprocessed_events(file_paths)
mne_events, events_mapp = mneprep.pd_to_mne_events(pd_events,
eeg.info['sfreq'])
# Epoching
epochs = mne.Epochs(eeg, mne_events, event_id=events_mapp, tmin=-3, tmax=3)
epochs.plot(scalings=scalings)
# PICKS
pick_hip = mnehelp.picks_all_localised(eeg, montage, 'Hi')
pick_perhead_all = mnehelp.picks_all(eeg, montage)
pick_perhead_hip_names = mne.pick_info(eeg.info, pick_hip)['ch_names']
# Playing
eeg.plot(scalings=scalings)
eeg.plot_psd(fmax=100, picks=pick_hip, average=False)
from functions import mne_prepping as mneprep
from functions import mne_loading as loader
from functions import paths
from mne.time_frequency import tfr_morlet
# %% Setup
base_path = 'E:/OneDrive/FGU/iEEG/Data'
participant = 'p136'
scalings = {'seeg': 5e2, 'ecg': 1e2, 'misc': 1e2}
FULL_EPOCH_TIME = (-1.5, 1.5)
file_paths = paths.prep_unity_alloego_files(base_path, participant)
EEG_TYPE = 'perElectrode'
# %% Raw eeg loading
eeg, montage = loader.load_eeg(file_paths, EEG_TYPE)
eeg.notch_filter(50)
# %% Epoching
pd_events = mneprep.load_preprocessed_events(file_paths)
mne_events, events_mapp = mneprep.pd_to_mne_events(pd_events,
eeg.info['sfreq'])
epochs = mne.Epochs(eeg, mne_events, event_id=events_mapp, tmin=-5, tmax=5)
# %% epoch visualisation and bad epoch removal
epochs.plot(n_epochs=10, n_channels=25, scalings=scalings)
append = '-' + EEG_TYPE
mneprep.write_bad_epochs(epochs, file_paths, append=append)
# %% Loading already selected