Initial commit

utils/ccs_eeg_semesterproject.py

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+import os
+import mne
+import numpy as np
+import pandas as pd
+from mne_bids import (BIDSPath, read_raw_bids)
+
+
+def _get_filepath(bids_root, subject_id, task):
+    bids_path = BIDSPath(subject=subject_id, task=task, session=task,
+                         datatype='eeg', suffix='eeg',
+                         root=bids_root)
+    # this is not a bids-conform file format, but a derivate/extension. Therefore we have to hack a bit
+    # Depending on path structure, this might push a warning.
+    fn = os.path.splitext(bids_path.fpath.__str__())[0]
+    assert (fn[-3:] == "eeg")
+    fn = fn[0:-3]
+    return fn
+
+
+def load_precomputed_ica(bids_root, subject_id, task):
+    # returns ICA and badComponents (starting at component = 0).
+    # Note the existance of add_ica_info in case you want to plot something.
+    fn = _get_filepath(bids_root, subject_id, task) + 'ica'
+
+    # import the eeglab ICA. I used eeglab because the "amica" ICA is a bit more powerful than runica
+    ica = mne.preprocessing.read_ica_eeglab(fn + '.set')
+    # ica = custom_read_eeglab_ica(fn+'.set')
+    # Potentially for plotting one might want to copy over the raw.info, but in this function we dont have access / dont want to load it
+    # ica.info = raw.info
+    ica._update_ica_names()
+    badComps = np.loadtxt(fn + '.tsv', delimiter="\t")
+    badComps -= 1  # start counting at 0
+
+    # if only a single component is in the file, we get an error here because it is an ndarray with n-dim = 0.
+    if len(badComps.shape) == 0:
+        badComps = [float(badComps)]
+    return ica, badComps
+
+
+def add_ica_info(raw, ica):
+    # This function exists due to a MNE bug: https://github.com/mne-tools/mne-python/issues/8581
+    # In case you want to plot your ICA components, this function will generate a ica.info
+    ch_raw = raw.info['ch_names']
+    ch_ica = ica.ch_names
+
+    ix = [k for k, c in enumerate(ch_raw) if c in ch_ica and not c in raw.info['bads']]
+    info = raw.info.copy()
+    mne.io.pick.pick_info(info, ix, copy=False)
+    ica.info = info
+
+    return ica
+
+
+def load_precomputed_badData(bids_root, subject_id, task):
+    # return precomputed annotations and bad channels (first channel = 0)
+
+    fn = _get_filepath(bids_root, subject_id, task)
+    print(fn)
+
+    tmp = pd.read_csv(fn + 'badSegments.csv')
+    # print(tmp)
+    annotations = mne.Annotations(tmp.onset, tmp.duration, tmp.description)
+    # Unfortunately MNE assumes that csv files are in milliseconds and only txt files in seconds.. wth?
+    # annotations = mne.read_annotations(fn+'badSegments.csv')
+    badChannels = np.loadtxt(fn + 'badChannels.tsv', delimiter='\t')
+    badChannels = badChannels.astype(int)
+    badChannels -= 1  # start counting at 0
+
+    # badChannels = [int(b) for b in badChannels]
+    return annotations, badChannels