from mne.fiff import Raw from mne.datasets import spm_face from mne.decoding import time_generalization data_path = spm_face.data_path() ############################################################################### # Load and filter data, set up epochs raw_fname = data_path + '/MEG/spm/SPM_CTF_MEG_example_faces%d_3D_raw.fif' raw = Raw(raw_fname % 1, preload=True) # Take first run raw.append(Raw(raw_fname % 2, preload=True)) # Take second run too picks = mne.fiff.pick_types(raw.info, meg=True, exclude='bads') raw.filter(1, 45, method='iir') events = mne.find_events(raw, stim_channel='UPPT001') event_id = {"faces": 1, "scrambled": 2} tmin, tmax = -0.1, 0.5 # Set up pick list picks = fiff.pick_types(raw.info, meg=True, eeg=False, stim=True, eog=True, ref_meg=False, exclude='bads') # Read epochs decim = 4 # decimate to make the example faster to run epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks, baseline=None, preload=True, reject=dict(mag=1.5e-12), decim=decim)
from mne.fiff import Raw from mne.datasets import spm_face from mne.decoding import time_generalization data_path = spm_face.data_path() ############################################################################### # Load and filter data, set up epochs raw_fname = data_path + '/MEG/spm/SPM_CTF_MEG_example_faces%d_3D_raw.fif' raw = Raw(raw_fname % 1, preload=True) # Take first run raw.append(Raw(raw_fname % 2, preload=True)) # Take second run too picks = mne.fiff.pick_types(raw.info, meg=True, exclude='bads') raw.filter(1, 45, method='iir') events = mne.find_events(raw, stim_channel='UPPT001') event_id = {"faces": 1, "scrambled": 2} tmin, tmax = -0.1, 0.5 # Set up pick list picks = fiff.pick_types(raw.info, meg=True, eeg=False, stim=True, eog=True, ref_meg=False, exclude='bads') # Read epochs
import mne from mne.fiff import Raw from mne.preprocessing.ica import ICA from mne.datasets import sample from mne.filter import band_pass_filter ############################################################################### # Setup paths and prepare raw data data_path = sample.data_path() raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' raw = Raw(raw_fname, preload=True) raw.filter(1, 45, n_jobs=2) picks = mne.fiff.pick_types(raw.info, meg=True, eeg=False, eog=False, stim=False, exclude='bads') ############################################################################### # Setup ICA seed decompose data, then access and plot sources. # Instead of the actual number of components here we pass a float value # between 0 and 1 to select n_components based on the percentage of # variance explained by the PCA components. ica = ICA(n_components=0.90, n_pca_components=None, max_pca_components=None, random_state=0) # Also we decide to use all PCA components before mixing back to sensor space.
filter_type = 'butter' filter_order = 4 n_jobs = 4 n_components=0.99 n_pca_components=1.0 max_pca_components=None ecg_ch_name = 'ECG 001' res_ch_name = 'STI 013' sti_ch_name = 'STI 014' eog_ch_name = 'EOG 002' picks = mne.fiff.pick_types(raw.info, meg=True, exclude='bads') #raw.filter(l_freq=1, h_freq=45, picks=picks, n_jobs=n_jobs) raw.filter(flow, fhigh, picks=picks, n_jobs=n_jobs, method='iir', iir_params={'ftype': filter_type, 'order': filter_order}) ica = ICA(n_components=n_components, n_pca_components=n_pca_components, max_pca_components=max_pca_components, random_state=0) ica.decompose_raw(raw, picks=picks, decim=3) ##################EOG 1st rejection#################################### eog_ch_idx = [raw.ch_names.index(eog_ch_name)] raw.filter(picks=eog_ch_idx, l_freq=1, h_freq=10) eog_scores = ica.find_sources_raw(raw, raw[eog_ch_idx][0]) eog_idx = np.where(np.abs(eog_scores) > 0.1)[0] ica.exclude += list(eog_idx)