mne-sample-meg-auditory

MEG data from auditory stimulation experiment using 305 sensors. The design matrix/forward operator is `data[:, :7498]`. The measurements for left stimulation are `data[:, 7498:7583]`. The measurements for right stimulation are `data[:, 7583:]`. The data was generated with the following script: ``` import mne import numpy as np from mne.datasets import sample from mne.inverse_sparse.mxne_inverse import _prepare_gain # this script used MNE 1.0.2 condition = "Right Auditory" data_path = sample.data_path() + '/MEG/sample' loose = 0 depth = 0.8 fwd_fname = data_path + '/sample_audvis-meg-eeg-oct-6-fwd.fif' ave_fname = data_path + '/sample_audvis-ave.fif' cov_fname = data_path + '/sample_audvis-shrunk-cov.fif' # Read noise covariance matrix noise_cov = mne.read_cov(cov_fname) # Handling forward solution forward = mne.read_forward_solution(fwd_fname) targets = {} for condition in ["Left Auditory", "Right Auditory"]: evoked = mne.read_evokeds( ave_fname, condition=condition, baseline=(None, 0)) evoked.crop(tmin=0.04, tmax=0.18) evoked = evoked.pick_types(eeg=False, meg=True) # Handle depth weighting and whitening (here is no weights) forward, gain, gain_info, whitener, _, _ = _prepare_gain( forward, evoked.info, noise_cov, pca=False, depth=depth, loose=loose, weights=None, weights_min=None, rank=None) # Select channels of interest sel = [evoked.ch_names.index(name) for name in gain_info['ch_names']] M = evoked.data[sel] # Whiten data M = whitener @ M targets[condition] = M # gain is independent of condition: data = np.hstack([gain, targets["Left Auditory"], targets["Right Auditory"]]) ```