# Authors: Alexandre Gramfort # Matti Hamalainen # Martin Luessi # # License: BSD (3-clause) from os import path import numpy as np from .io.meas_info import Info from .io.pick import _pick_data_channels, pick_types from .utils import logger, verbose, _get_stim_channel _SELECTIONS = ['Vertex', 'Left-temporal', 'Right-temporal', 'Left-parietal', 'Right-parietal', 'Left-occipital', 'Right-occipital', 'Left-frontal', 'Right-frontal'] _EEG_SELECTIONS = ['EEG 1-32', 'EEG 33-64', 'EEG 65-96', 'EEG 97-128'] @verbose def read_selection(name, fname=None, info=None, verbose=None): """Read channel selection from file. By default, the selections used in ``mne_browse_raw`` are supported. Additional selections can be added by specifying a selection file (e.g. produced using ``mne_browse_raw``) using the ``fname`` parameter. The ``name`` parameter can be a string or a list of string. The returned selection will be the combination of all selections in the file where (at least) one element in name is a substring of the selection name in the file. For example, ``name=['temporal', 'Right-frontal']`` will produce a combination of ``'Left-temporal'``, ``'Right-temporal'``, and ``'Right-frontal'``. The included selections are: * ``'Vertex'`` * ``'Left-temporal'`` * ``'Right-temporal'`` * ``'Left-parietal'`` * ``'Right-parietal'`` * ``'Left-occipital'`` * ``'Right-occipital'`` * ``'Left-frontal'`` * ``'Right-frontal'`` Parameters ---------- name : str or list of str Name of the selection. If is a list, the selections are combined. fname : str Filename of the selection file (if None, built-in selections are used). info : instance of Info Measurement info file, which will be used to determine the spacing of channel names to return, e.g. ``'MEG 0111'`` for old Neuromag systems and ``'MEG0111'`` for new ones. verbose : bool, str, int, or None If not None, override default verbose level (see :func:`mne.verbose` and :ref:`Logging documentation ` for more). Returns ------- sel : list of string List with channel names in the selection. """ # convert name to list of string if not isinstance(name, (list, tuple)): name = [name] if isinstance(info, Info): picks = pick_types(info, meg=True, exclude=()) if len(picks) > 0 and ' ' not in info['ch_names'][picks[0]]: spacing = 'new' else: spacing = 'old' elif info is not None: raise TypeError('info must be an instance of Info or None, not %s' % (type(info),)) else: # info is None spacing = 'old' # use built-in selections by default if fname is None: fname = path.join(path.dirname(__file__), 'data', 'mne_analyze.sel') if not path.isfile(fname): raise ValueError('The file %s does not exist.' % fname) # use this to make sure we find at least one match for each name name_found = dict((n, False) for n in name) with open(fname, 'r') as fid: sel = [] for line in fid: line = line.strip() # skip blank lines and comments if len(line) == 0 or line[0] == '#': continue # get the name of the selection in the file pos = line.find(':') if pos < 0: logger.info('":" delimiter not found in selections file, ' 'skipping line') continue sel_name_file = line[:pos] # search for substring match with name provided for n in name: if sel_name_file.find(n) >= 0: sel.extend(line[pos + 1:].split('|')) name_found[n] = True break # make sure we found at least one match for each name for n, found in name_found.items(): if not found: raise ValueError('No match for selection name "%s" found' % n) # make the selection a sorted list with unique elements sel = list(set(sel)) sel.sort() if spacing == 'new': # "new" or "old" by now, "old" is default sel = [s.replace('MEG ', 'MEG') for s in sel] return sel def _divide_to_regions(info, add_stim=True): """Divide channels to regions by positions.""" from scipy.stats import zscore picks = _pick_data_channels(info, exclude=[]) chs_in_lobe = len(picks) // 4 pos = np.array([ch['loc'][:3] for ch in info['chs']]) x, y, z = pos.T frontal = picks[np.argsort(y[picks])[-chs_in_lobe:]] picks = np.setdiff1d(picks, frontal) occipital = picks[np.argsort(y[picks])[:chs_in_lobe]] picks = np.setdiff1d(picks, occipital) temporal = picks[np.argsort(z[picks])[:chs_in_lobe]] picks = np.setdiff1d(picks, temporal) lt, rt = _divide_side(temporal, x) lf, rf = _divide_side(frontal, x) lo, ro = _divide_side(occipital, x) lp, rp = _divide_side(picks, x) # Parietal lobe from the remaining picks. # Because of the way the sides are divided, there may be outliers in the # temporal lobes. Here we switch the sides for these outliers. For other # lobes it is not a big problem because of the vicinity of the lobes. with np.errstate(invalid='ignore'): # invalid division, greater compare zs = np.abs(zscore(x[rt])) outliers = np.array(rt)[np.where(zs > 2.)[0]] rt = list(np.setdiff1d(rt, outliers)) with np.errstate(invalid='ignore'): # invalid division, greater compare zs = np.abs(zscore(x[lt])) outliers = np.append(outliers, (np.array(lt)[np.where(zs > 2.)[0]])) lt = list(np.setdiff1d(lt, outliers)) l_mean = np.mean(x[lt]) r_mean = np.mean(x[rt]) for outlier in outliers: if abs(l_mean - x[outlier]) < abs(r_mean - x[outlier]): lt.append(outlier) else: rt.append(outlier) if add_stim: stim_ch = _get_stim_channel(None, info, raise_error=False) if len(stim_ch) > 0: for region in [lf, rf, lo, ro, lp, rp, lt, rt]: region.append(info['ch_names'].index(stim_ch[0])) return {'Left-frontal': lf, 'Right-frontal': rf, 'Left-parietal': lp, 'Right-parietal': rp, 'Left-occipital': lo, 'Right-occipital': ro, 'Left-temporal': lt, 'Right-temporal': rt} def _divide_side(lobe, x): """Make a separation between left and right lobe evenly.""" lobe = np.asarray(lobe) median = np.median(x[lobe]) left = lobe[np.where(x[lobe] < median)[0]] right = lobe[np.where(x[lobe] > median)[0]] medians = np.where(x[lobe] == median)[0] left = np.sort(np.concatenate([left, lobe[medians[1::2]]])) right = np.sort(np.concatenate([right, lobe[medians[::2]]])) return list(left), list(right)