from mne import find_events, fit_dipole
from mne.datasets.brainstorm import bst_phantom_elekta
from mne.io import read_raw_fif
print(__doc__)
###############################################################################
# Plot the phantom data, lowpassed to get rid of high-frequency artifacts.
# We also crop to a single 10-second segment for speed.
# Notice that there are two large flux jumps on channel 1522 that could
# spread to other channels when performing subsequent spatial operations
# (e.g., Maxwell filtering, SSP, or ICA).
dipole_number = 1
data_path = bst_phantom_elekta.data_path()
raw = read_raw_fif(
op.join(data_path, 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif'))
raw.crop(40., 50.).load_data()
order = list(range(160, 170))
raw.copy().filter(0., 40.).plot(order=order, n_channels=10)
###############################################################################
# Now we can clean the data with OTP, lowpass, and plot. The flux jumps have
# been suppressed alongside the random sensor noise.
raw_clean = mne.preprocessing.oversampled_temporal_projection(raw)
raw_clean.filter(0., 40.)
raw_clean.plot(order=order, n_channels=10)
import numpy as np import matplotlib.pyplot as plt import mne from mne import find_events, fit_dipole from mne.datasets.brainstorm import bst_phantom_elekta from mne.io import read_raw_fif from mayavi import mlab print(__doc__) ############################################################################### # The data were collected with an Elekta Neuromag VectorView system at 1000 Hz # and low-pass filtered at 330 Hz. Here the medium-amplitude (200 nAm) data # are read to construct instances of :class:`mne.io.Raw`. data_path = bst_phantom_elekta.data_path() raw_fname = op.join(data_path, 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif') raw = read_raw_fif(raw_fname) ############################################################################### # Data channel array consisted of 204 MEG planor gradiometers, # 102 axial magnetometers, and 3 stimulus channels. Let's get the events # for the phantom, where each dipole (1-32) gets its own event: events = find_events(raw, 'STI201') raw.plot(events=events) raw.info['bads'] = ['MEG2421'] ############################################################################### # The data have strong line frequency (60 Hz and harmonics) and cHPI coil
import os.path as op import numpy as np import matplotlib.pyplot as plt import mne from mne import find_events, fit_dipole from mne.datasets.brainstorm import bst_phantom_elekta from mne.io import read_raw_fif print(__doc__) # %% # The data were collected with an Elekta Neuromag VectorView system at 1000 Hz # and low-pass filtered at 330 Hz. Here the medium-amplitude (200 nAm) data # are read to construct instances of :class:`mne.io.Raw`. data_path = bst_phantom_elekta.data_path(verbose=True) subject = 'sample' raw_fname = op.join(data_path, 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif') raw = read_raw_fif(raw_fname) # %% # Data channel array consisted of 204 MEG planor gradiometers, # 102 axial magnetometers, and 3 stimulus channels. Let's get the events # for the phantom, where each dipole (1-32) gets its own event: events = find_events(raw, 'STI201') raw.plot(events=events) raw.info['bads'] = ['MEG1933', 'MEG2421'] # %%
import numpy as np import matplotlib.pyplot as plt import mne from mne import find_events, fit_dipole from mne.datasets.brainstorm import bst_phantom_elekta from mne.io import read_raw_fif from mayavi import mlab print(__doc__) ############################################################################### # The data were collected with an Elekta Neuromag VectorView system at 1000 Hz # and low-pass filtered at 330 Hz. Here the medium-amplitude (200 nAm) data # are read to construct instances of :class:`mne.io.Raw`. data_path = bst_phantom_elekta.data_path(verbose=True) raw_fname = op.join(data_path, 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif') raw = read_raw_fif(raw_fname).load_data() ############################################################################### # Data channel array consisted of 204 MEG planor gradiometers, # 102 axial magnetometers, and 3 stimulus channels. Let's get the events # for the phantom, where each dipole (1-32) gets its own event: events = find_events(raw, 'STI201') raw.plot(events=events) raw.info['bads'] = ['MEG1933', 'MEG2421'] ############################################################################### # The data have strong line frequency (60 Hz and harmonics) and cHPI coil
