from mne.datasets import hf_sef
from matplotlib import pyplot as plt
from groupmne import group_model
from groupmne.inverse import compute_group_inverse
##########################################################
# Download and process MEG data
# -----------------------------
#
# For this example, we use the HF somatosensory dataset [2].
# We need the raw data to estimate the noise covariance
# since only average MEG data (and MRI) are provided in "evoked".
# The data will be downloaded in the same location
_ = hf_sef.data_path("raw")
data_path = hf_sef.data_path("evoked")
meg_path = data_path + "/MEG/"
data_path = op.expanduser(data_path)
subjects_dir = data_path + "/subjects/"
os.environ['SUBJECTS_DIR'] = subjects_dir
raw_name_s = [
meg_path + s
for s in ["subject_a/sef_right_raw.fif", "subject_b/hf_sef_15min_raw.fif"]
]
def process_meg(raw_name):
raw = mne.io.read_raw_fif(raw_name)
from mne.datasets import hf_sef, sample # download it if not found hf_sef.data_path() sample.data_path()
============== HF-SEF dataset ============== This example looks at high frequency SEF responses. """ # Author: Jussi Nurminen ([email protected]) # # License: BSD (3-clause) import mne import os from mne.datasets import hf_sef fname_evoked = os.path.join(hf_sef.data_path(), 'MEG/subject_b/hf_sef_15min-ave.fif') print(__doc__) ############################################################################### # Read evoked data evoked = mne.Evoked(fname_evoked) ############################################################################### # Create a highpass filtered version evoked_hp = evoked.copy() evoked_hp.filter(l_freq=300, h_freq=None, fir_design='firwin') ############################################################################### # Compare high-pass filtered and unfiltered data on a single channel
HF-SEF dataset ============== This example looks at high frequency SEF responses. """ # Author: Jussi Nurminen ([email protected]) # # License: BSD (3-clause) import mne import os from mne.datasets import hf_sef fname_evoked = os.path.join(hf_sef.data_path(), 'MEG/subject_b/hf_sef_15min-ave.fif') print(__doc__) ############################################################################### # Read evoked data evoked = mne.Evoked(fname_evoked) ############################################################################### # Create a highpass filtered version evoked_hp = evoked.copy() evoked_hp.filter(l_freq=300, h_freq=None, fir_design='firwin') ############################################################################### # Compare high-pass filtered and unfiltered data on a single channel