Initial commit

plotting.py

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+import mne
+from mne.preprocessing import create_eog_epochs
+
+from mne_bids import BIDSPath, read_raw_bids
+from utils.ccs_eeg_utils import read_annotations_core
+from utils.file_utils import get_epochs
+
+
+def load_unprocessed_subject(subject, dataset):
+    """
+    Load the eeg data of a subject
+    :param subject: The subject of which the data will be loaded
+    :param dataset: The dataset which will be loaded
+    :return: the subject data
+    """
+    bids_path = BIDSPath(subject=subject, task=dataset, session=dataset, datatype='eeg', suffix='eeg',
+                         root='Dataset\\' + dataset)
+    raw = read_raw_bids(bids_path)
+    # Add annotations
+    read_annotations_core(bids_path, raw)
+    return raw
+
+
+def filter_data(raw):
+    """
+    Filter the data of a single subject with a bandpass filter.
+    The lower bound ist 0.5Hz to compensate the slow drifts.
+    The upper bound is 50Hz to compensate the high frequencies, including the power line spike at 60Hz
+    :param raw: The data to be filtered
+    :return: The filtered data
+    """
+    raw.filter(0.5, 48, fir_design='firwin')
+    return raw
+
+
+def plot_filter_data():
+    ds = 'N170'
+    for subj in ['014']:
+        data = load_unprocessed_subject(subj, ds)
+        data.load_data()
+        # data.plot(n_channels=len(data.ch_names), block=True, scalings=40e-6)
+        filter_data(data)
+        fig = mne.viz.plot_raw_psd(data, fmax=80, average=True, show=False)
+        fig.savefig("plots/frequency_filtered_subj_" + subj + "_48Hz.png")
+        # data.plot(n_channels=len(data.ch_names), block=True, scalings=40e-6)
+
+
+def plot_filter_data_epoched(subj):
+    ds = 'N170'
+    data = load_unprocessed_subject(subj, ds)
+    data.load_data()
+    filter_data(data)
+    get_epochs(data)[0].average().plot()
+
+
+def plot_cleaning():
+    ds = 'N170'
+    for subj in ['014']:
+        data = load_unprocessed_subject(subj, ds)
+        data.load_data()
+        filter_data(data)
+        folder = "Dataset\\" + ds + "\\sub-" + subj + "\\ses-" + ds + "\\eeg\\"
+        filepath = folder + "sub-" + subj + "_task-" + ds
+        print(filepath)
+        ann = mne.read_annotations(filepath + "_" + "badannotations.csv")
+        data.annotations.append(ann.onset, ann.duration, ann.description)
+        data.plot(n_channels=len(data.ch_names), block=True, scalings=40e-6)
+
+
+def plot_ica():
+    ds = 'N170'
+    for subj in ['014']:
+        data = load_unprocessed_subject(subj, ds)
+        folder = "Dataset\\" + ds + "\\sub-" + subj + "\\ses-" + ds + "\\eeg\\"
+        filepath = folder + "sub-" + subj + "_task-" + ds
+        ann = mne.read_annotations(filepath + "_" + "badannotations.csv")
+        data.annotations.append(ann.onset, ann.duration, ann.description)
+        data.load_data()
+        data.set_channel_types({'HEOG_left': 'eog', 'HEOG_right': 'eog', 'VEOG_lower': 'eog'})
+        data.set_montage('standard_1020', match_case=False)
+        ica_raw = data.copy()
+        ica_raw.filter(l_freq=1, h_freq=None)
+
+        # Then run ICA
+        ica = mne.preprocessing.ICA(method="fastica",
+                                    random_state=123)  # Use a random state for reproducable results #TODO Old Random state 123 or new one?
+        ica.fit(ica_raw, verbose=True)
+        ica_raw.load_data()
+        # ica.plot_components(inst=ica_raw, ch_type='eeg', contours=0, topomap_args={'extrapolate': 'head'},
+        #                    psd_args={'fmin': 0, 'fmax': 80})
+        ica.plot_sources(ica_raw)
+        ica.plot_properties(inst=ica_raw, dB=False, topomap_args={'extrapolate': 'head', 'contours': 0},
+                            psd_args={'fmin': 3, 'fmax': 50}, picks=['eeg'])
+
+
+def plot_joint_eog_plots():
+    ds = 'N170'
+    for subj in ['014']:
+        data = load_unprocessed_subject(subj, ds)
+        data.load_data()
+        data.set_channel_types({'HEOG_left': 'eog', 'HEOG_right': 'eog', 'VEOG_lower': 'eog'})
+        data.set_montage('standard_1020', match_case=False)
+        eog_evoked = create_eog_epochs(data).average()
+        eog_evoked.apply_baseline(baseline=(None, -0.2))
+        eog_evoked.plot_joint()
+
+
+# plot_ica()
+# plot_joint_eog_plots()
+plot_filter_data_epoched('003')