# Authors: Alexandre Gramfort # Denis Engemann # Martin Luessi # Eric Larson # Marijn van Vliet # Jona Sassenhagen # Teon Brooks # Christian Brodbeck # Stefan Appelhoff # Joan Massich # # License: Simplified BSD from collections import OrderedDict from dataclasses import dataclass from copy import deepcopy import os.path as op import re import numpy as np from ..defaults import HEAD_SIZE_DEFAULT from .._freesurfer import get_mni_fiducials from ..viz import plot_montage from ..transforms import (apply_trans, get_ras_to_neuromag_trans, _sph_to_cart, _topo_to_sph, _frame_to_str, Transform, _verbose_frames, _fit_matched_points, _quat_to_affine, _ensure_trans) from ..io._digitization import (_count_points_by_type, _ensure_fiducials_head, _get_dig_eeg, _make_dig_points, write_dig, _read_dig_fif, _format_dig_points, _get_fid_coords, _coord_frame_const, _get_data_as_dict_from_dig) from ..io.meas_info import create_info from ..io.open import fiff_open from ..io.pick import pick_types, _picks_to_idx, channel_type from ..io.constants import FIFF, CHANNEL_LOC_ALIASES from ..utils import (warn, copy_function_doc_to_method_doc, _pl, verbose, _check_option, _validate_type, _check_fname, _on_missing, fill_doc, _docdict) from ._dig_montage_utils import _read_dig_montage_egi from ._dig_montage_utils import _parse_brainvision_dig_montage @dataclass class _BuiltinStandardMontage: name: str description: str _BUILTIN_STANDARD_MONTAGES = [ _BuiltinStandardMontage( name='standard_1005', description='Electrodes are named and positioned according to the ' 'international 10-05 system (343+3 locations)', ), _BuiltinStandardMontage( name='standard_1020', description='Electrodes are named and positioned according to the ' 'international 10-20 system (94+3 locations)', ), _BuiltinStandardMontage( name='standard_alphabetic', description='Electrodes are named with LETTER-NUMBER combinations ' '(A1, B2, F4, …) (65+3 locations)', ), _BuiltinStandardMontage( name='standard_postfixed', description='Electrodes are named according to the international ' '10-20 system using postfixes for intermediate positions ' '(100+3 locations)', ), _BuiltinStandardMontage( name='standard_prefixed', description='Electrodes are named according to the international ' '10-20 system using prefixes for intermediate positions ' '(74+3 locations)', ), _BuiltinStandardMontage( name='standard_primed', description="Electrodes are named according to the international " "10-20 system using prime marks (' and '') for " "intermediate positions (100+3 locations)", ), _BuiltinStandardMontage( name='biosemi16', description='BioSemi cap with 16 electrodes (16+3 locations)', ), _BuiltinStandardMontage( name='biosemi32', description='BioSemi cap with 32 electrodes (32+3 locations)', ), _BuiltinStandardMontage( name='biosemi64', description='BioSemi cap with 64 electrodes (64+3 locations)', ), _BuiltinStandardMontage( name='biosemi128', description='BioSemi cap with 128 electrodes (128+3 locations)', ), _BuiltinStandardMontage( name='biosemi160', description='BioSemi cap with 160 electrodes (160+3 locations)', ), _BuiltinStandardMontage( name='biosemi256', description='BioSemi cap with 256 electrodes (256+3 locations)', ), _BuiltinStandardMontage( name='easycap-M1', description='EasyCap with 10-05 electrode names (74 locations)', ), _BuiltinStandardMontage( name='easycap-M10', description='EasyCap with numbered electrodes (61 locations)', ), _BuiltinStandardMontage( name='EGI_256', description='Geodesic Sensor Net (256 locations)', ), _BuiltinStandardMontage( name='GSN-HydroCel-32', description='HydroCel Geodesic Sensor Net and Cz (33+3 locations)', ), _BuiltinStandardMontage( name='GSN-HydroCel-64_1.0', description='HydroCel Geodesic Sensor Net (64+3 locations)', ), _BuiltinStandardMontage( name='GSN-HydroCel-65_1.0', description='HydroCel Geodesic Sensor Net and Cz (65+3 locations)', ), _BuiltinStandardMontage( name='GSN-HydroCel-128', description='HydroCel Geodesic Sensor Net (128+3 locations)', ), _BuiltinStandardMontage( name='GSN-HydroCel-129', description='HydroCel Geodesic Sensor Net and Cz (129+3 locations)', ), _BuiltinStandardMontage( name='GSN-HydroCel-256', description='HydroCel Geodesic Sensor Net (256+3 locations)', ), _BuiltinStandardMontage( name='GSN-HydroCel-257', description='HydroCel Geodesic Sensor Net and Cz (257+3 locations)', ), _BuiltinStandardMontage( name='mgh60', description='The (older) 60-channel cap used at MGH (60+3 locations)', ), _BuiltinStandardMontage( name='mgh70', description='The (newer) 70-channel BrainVision cap used at MGH ' '(70+3 locations)', ), _BuiltinStandardMontage( name='artinis-octamon', description='Artinis OctaMon fNIRS (8 sources, 2 detectors)', ), _BuiltinStandardMontage( name='artinis-brite23', description='Artinis Brite23 fNIRS (11 sources, 7 detectors)', ), _BuiltinStandardMontage( name='brainproducts-RNP-BA-128', description='Brain Products with 10-10 electrode names (128 channels)', ) ] def _check_get_coord_frame(dig): dig_coord_frames = sorted(set(d['coord_frame'] for d in dig)) if len(dig_coord_frames) != 1: raise RuntimeError( 'Only a single coordinate frame in dig is supported, got ' f'{dig_coord_frames}') return _frame_to_str[dig_coord_frames.pop()] if dig_coord_frames else None def get_builtin_montages(*, descriptions=False): """Get a list of all standard montages shipping with MNE-Python. The names of the montages can be passed to :func:`make_standard_montage`. Parameters ---------- descriptions : bool Whether to return not only the montage names, but also their corresponding descriptions. If ``True``, a list of tuples is returned, where the first tuple element is the montage name and the second is the montage description. If ``False`` (default), only the names are returned. .. versionadded:: 1.1 Returns ------- montages : list of str | list of tuple If ``descriptions=False``, the names of all builtin montages that can be used by :func:`make_standard_montage`. If ``descriptions=True``, a list of tuples ``(name, description)``. """ if descriptions: return [ (m.name, m.description) for m in _BUILTIN_STANDARD_MONTAGES ] else: return [m.name for m in _BUILTIN_STANDARD_MONTAGES] def make_dig_montage(ch_pos=None, nasion=None, lpa=None, rpa=None, hsp=None, hpi=None, coord_frame='unknown'): r"""Make montage from arrays. Parameters ---------- ch_pos : dict | None Dictionary of channel positions. Keys are channel names and values are 3D coordinates - array of shape (3,) - in native digitizer space in m. nasion : None | array, shape (3,) The position of the nasion fiducial point. This point is assumed to be in the native digitizer space in m. lpa : None | array, shape (3,) The position of the left periauricular fiducial point. This point is assumed to be in the native digitizer space in m. rpa : None | array, shape (3,) The position of the right periauricular fiducial point. This point is assumed to be in the native digitizer space in m. hsp : None | array, shape (n_points, 3) This corresponds to an array of positions of the headshape points in 3d. These points are assumed to be in the native digitizer space in m. hpi : None | array, shape (n_hpi, 3) This corresponds to an array of HPI points in the native digitizer space. They only necessary if computation of a ``compute_dev_head_t`` is True. coord_frame : str The coordinate frame of the points. Usually this is ``'unknown'`` for native digitizer space. Other valid values are: ``'head'``, ``'meg'``, ``'mri'``, ``'mri_voxel'``, ``'mni_tal'``, ``'ras'``, ``'fs_tal'``, ``'ctf_head'``, and ``'ctf_meg'``. .. note:: For custom montages without fiducials, this parameter must be set to ``'head'``. Returns ------- montage : instance of DigMontage The montage. See Also -------- DigMontage read_dig_captrak read_dig_egi read_dig_fif read_dig_localite read_dig_polhemus_isotrak """ _validate_type(ch_pos, (dict, None), 'ch_pos') if ch_pos is None: ch_names = None else: ch_names = list(ch_pos) dig = _make_dig_points( nasion=nasion, lpa=lpa, rpa=rpa, hpi=hpi, extra_points=hsp, dig_ch_pos=ch_pos, coord_frame=coord_frame ) return DigMontage(dig=dig, ch_names=ch_names) class DigMontage(object): """Montage for digitized electrode and headshape position data. .. warning:: Montages are typically created using one of the helper functions in the ``See Also`` section below instead of instantiating this class directly. Parameters ---------- dig : list of dict The object containing all the dig points. ch_names : list of str The names of the EEG channels. See Also -------- read_dig_captrak read_dig_dat read_dig_egi read_dig_fif read_dig_hpts read_dig_localite read_dig_polhemus_isotrak make_dig_montage Notes ----- .. versionadded:: 0.9.0 """ def __init__(self, *, dig=None, ch_names=None): dig = list() if dig is None else dig _validate_type(item=dig, types=list, item_name='dig') ch_names = list() if ch_names is None else ch_names n_eeg = sum([1 for d in dig if d['kind'] == FIFF.FIFFV_POINT_EEG]) if n_eeg != len(ch_names): raise ValueError( 'The number of EEG channels (%d) does not match the number' ' of channel names provided (%d)' % (n_eeg, len(ch_names)) ) self.dig = dig self.ch_names = ch_names def __repr__(self): """Return string representation.""" n_points = _count_points_by_type(self.dig) return ('').format(**n_points) @copy_function_doc_to_method_doc(plot_montage) def plot(self, scale_factor=20, show_names=True, kind='topomap', show=True, sphere=None, verbose=None): return plot_montage(self, scale_factor=scale_factor, show_names=show_names, kind=kind, show=show, sphere=sphere) @fill_doc def rename_channels(self, mapping, allow_duplicates=False): """Rename the channels. Parameters ---------- %(mapping_rename_channels_duplicates)s Returns ------- inst : instance of DigMontage The instance. Operates in-place. """ from .channels import rename_channels temp_info = create_info(list(self._get_ch_pos()), 1000., 'eeg') rename_channels(temp_info, mapping, allow_duplicates) self.ch_names = temp_info['ch_names'] @verbose def save(self, fname, *, overwrite=False, verbose=None): """Save digitization points to FIF. Parameters ---------- fname : path-like The filename to use. Should end in .fif or .fif.gz. %(overwrite)s %(verbose)s """ coord_frame = _check_get_coord_frame(self.dig) write_dig(fname, self.dig, coord_frame, overwrite=overwrite) def __iadd__(self, other): """Add two DigMontages in place. Notes ----- Two DigMontages can only be added if there are no duplicated ch_names and if fiducials are present they should share the same coordinate system and location values. """ def is_fid_defined(fid): return not ( fid.nasion is None and fid.lpa is None and fid.rpa is None ) # Check for none duplicated ch_names ch_names_intersection = set(self.ch_names).intersection(other.ch_names) if ch_names_intersection: raise RuntimeError(( "Cannot add two DigMontage objects if they contain duplicated" " channel names. Duplicated channel(s) found: {}." ).format( ', '.join(['%r' % v for v in sorted(ch_names_intersection)]) )) # Check for unique matching fiducials self_fid, self_coord = _get_fid_coords(self.dig) other_fid, other_coord = _get_fid_coords(other.dig) if is_fid_defined(self_fid) and is_fid_defined(other_fid): if self_coord != other_coord: raise RuntimeError('Cannot add two DigMontage objects if ' 'fiducial locations are not in the same ' 'coordinate system.') for kk in self_fid: if not np.array_equal(self_fid[kk], other_fid[kk]): raise RuntimeError('Cannot add two DigMontage objects if ' 'fiducial locations do not match ' '(%s)' % kk) # keep self self.dig = _format_dig_points( self.dig + [d for d in other.dig if d['kind'] != FIFF.FIFFV_POINT_CARDINAL] ) else: self.dig = _format_dig_points(self.dig + other.dig) self.ch_names += other.ch_names return self def copy(self): """Copy the DigMontage object. Returns ------- dig : instance of DigMontage The copied DigMontage instance. """ return deepcopy(self) def __add__(self, other): """Add two DigMontages.""" out = self.copy() out += other return out def __eq__(self, other): """Compare different DigMontage objects for equality. Returns ------- Boolean output from comparison of .dig """ return self.dig == other.dig and self.ch_names == other.ch_names def _get_ch_pos(self): pos = [d['r'] for d in _get_dig_eeg(self.dig)] assert len(self.ch_names) == len(pos) return OrderedDict(zip(self.ch_names, pos)) def _get_dig_names(self): NAMED_KIND = (FIFF.FIFFV_POINT_EEG,) is_eeg = np.array([d['kind'] in NAMED_KIND for d in self.dig]) assert len(self.ch_names) == is_eeg.sum() dig_names = [None] * len(self.dig) for ch_name_idx, dig_idx in enumerate(np.where(is_eeg)[0]): dig_names[dig_idx] = self.ch_names[ch_name_idx] return dig_names def get_positions(self): """Get all channel and fiducial positions. Returns ------- positions : dict A dictionary of the positions for channels (``ch_pos``), coordinate frame (``coord_frame``), nasion (``nasion``), left preauricular point (``lpa``), right preauricular point (``rpa``), Head Shape Polhemus (``hsp``), and Head Position Indicator(``hpi``). E.g.:: { 'ch_pos': {'EEG061': [0, 0, 0]}, 'nasion': [0, 0, 1], 'coord_frame': 'mni_tal', 'lpa': [0, 1, 0], 'rpa': [1, 0, 0], 'hsp': None, 'hpi': None } """ # get channel positions as dict ch_pos = self._get_ch_pos() # get coordframe and fiducial coordinates montage_bunch = _get_data_as_dict_from_dig(self.dig) coord_frame = _frame_to_str.get(montage_bunch.coord_frame) # return dictionary positions = dict( ch_pos=ch_pos, coord_frame=coord_frame, nasion=montage_bunch.nasion, lpa=montage_bunch.lpa, rpa=montage_bunch.rpa, hsp=montage_bunch.hsp, hpi=montage_bunch.hpi, ) return positions @verbose def apply_trans(self, trans, verbose=None): """Apply a transformation matrix to the montage. Parameters ---------- trans : instance of mne.transforms.Transform The transformation matrix to be applied. %(verbose)s """ _validate_type(trans, Transform, 'trans') coord_frame = self.get_positions()['coord_frame'] trans = _ensure_trans(trans, fro=coord_frame, to=trans['to']) for d in self.dig: d['r'] = apply_trans(trans, d['r']) d['coord_frame'] = trans['to'] @verbose def add_estimated_fiducials(self, subject, subjects_dir=None, verbose=None): """Estimate fiducials based on FreeSurfer ``fsaverage`` subject. This takes a montage with the ``mri`` coordinate frame, corresponding to the FreeSurfer RAS (xyz in the volume) T1w image of the specific subject. It will call :func:`mne.coreg.get_mni_fiducials` to estimate LPA, RPA and Nasion fiducial points. Parameters ---------- %(subject)s %(subjects_dir)s %(verbose)s Returns ------- inst : instance of DigMontage The instance, modified in-place. See Also -------- :ref:`tut-source-alignment` Notes ----- Since MNE uses the FIF data structure, it relies on the ``head`` coordinate frame. Any coordinate frame can be transformed to ``head`` if the fiducials (i.e. LPA, RPA and Nasion) are defined. One can use this function to estimate those fiducials and then use ``mne.channels.compute_native_head_t(montage)`` to get the head <-> MRI transform. """ # get coordframe and fiducial coordinates montage_bunch = _get_data_as_dict_from_dig(self.dig) # get the coordinate frame and check that it's MRI if montage_bunch.coord_frame != FIFF.FIFFV_COORD_MRI: raise RuntimeError( f'Montage should be in the "mri" coordinate frame ' f'to use `add_estimated_fiducials`. The current coordinate ' f'frame is {montage_bunch.coord_frame}') # estimate LPA, nasion, RPA from FreeSurfer fsaverage fids_mri = list(get_mni_fiducials(subject, subjects_dir)) # add those digpoints to front of montage self.dig = fids_mri + self.dig return self @verbose def add_mni_fiducials(self, subjects_dir=None, verbose=None): """Add fiducials to a montage in MNI space. Parameters ---------- %(subjects_dir)s %(verbose)s Returns ------- inst : instance of DigMontage The instance, modified in-place. Notes ----- ``fsaverage`` is in MNI space and so its fiducials can be added to a montage in "mni_tal". MNI is an ACPC-aligned coordinate system (the posterior commissure is the origin) so since BIDS requires channel locations for ECoG, sEEG and DBS to be in ACPC space, this function can be used to allow those coordinate to be transformed to "head" space (origin between LPA and RPA). """ montage_bunch = _get_data_as_dict_from_dig(self.dig) # get the coordinate frame and check that it's MNI TAL if montage_bunch.coord_frame != FIFF.FIFFV_MNE_COORD_MNI_TAL: raise RuntimeError( f'Montage should be in the "mni_tal" coordinate frame ' f'to use `add_estimated_fiducials`. The current coordinate ' f'frame is {montage_bunch.coord_frame}') fids_mni = get_mni_fiducials('fsaverage', subjects_dir) for fid in fids_mni: # "mri" and "mni_tal" are equivalent for fsaverage assert fid['coord_frame'] == FIFF.FIFFV_COORD_MRI fid['coord_frame'] = FIFF.FIFFV_MNE_COORD_MNI_TAL self.dig = fids_mni + self.dig return self @verbose def remove_fiducials(self, verbose=None): """Remove the fiducial points from a montage. Parameters ---------- %(verbose)s Returns ------- inst : instance of DigMontage The instance, modified in-place. Notes ----- MNE will transform a montage to the internal "head" coordinate frame if the fiducials are present. Under most circumstances, this is ideal as it standardizes the coordinate frame for things like plotting. However, in some circumstances, such as saving a ``raw`` with intracranial data to BIDS format, the coordinate frame should not be changed by removing fiducials. """ for d in self.dig.copy(): if d['kind'] == FIFF.FIFFV_POINT_CARDINAL: self.dig.remove(d) return self VALID_SCALES = dict(mm=1e-3, cm=1e-2, m=1) def _check_unit_and_get_scaling(unit): _check_option('unit', unit, sorted(VALID_SCALES.keys())) return VALID_SCALES[unit] def transform_to_head(montage): """Transform a DigMontage object into head coordinate. Parameters ---------- montage : instance of DigMontage The montage. Returns ------- montage : instance of DigMontage The montage after transforming the points to head coordinate system. Notes ----- This function requires that the LPA, RPA and Nasion fiducial points are available. If they are not, they will be added based by projecting the fiducials onto a sphere with radius equal to the average distance of each point to the origin (in the given coordinate frame). This function assumes that all fiducial points are in the same coordinate frame (e.g. 'unknown') and it will convert all the point in this coordinate system to Neuromag head coordinate system. .. versionchanged:: 1.2 Fiducial points will be added automatically if the montage does not have them. """ # Get fiducial points and their coord_frame native_head_t = compute_native_head_t(montage) montage = montage.copy() # to avoid inplace modification if native_head_t['from'] != FIFF.FIFFV_COORD_HEAD: for d in montage.dig: if d['coord_frame'] == native_head_t['from']: d['r'] = apply_trans(native_head_t, d['r']) d['coord_frame'] = FIFF.FIFFV_COORD_HEAD _ensure_fiducials_head(montage.dig) return montage def read_dig_dat(fname): r"""Read electrode positions from a ``*.dat`` file. .. Warning:: This function was implemented based on ``*.dat`` files available from `Compumedics `__ and might not work as expected with novel files. If it does not read your files correctly please contact the mne-python developers. Parameters ---------- fname : path-like File from which to read electrode locations. Returns ------- montage : DigMontage The montage. See Also -------- read_dig_captrak read_dig_dat read_dig_egi read_dig_fif read_dig_hpts read_dig_localite read_dig_polhemus_isotrak make_dig_montage Notes ----- ``*.dat`` files are plain text files and can be inspected and amended with a plain text editor. """ from ._standard_montage_utils import _check_dupes_odict fname = _check_fname(fname, overwrite='read', must_exist=True) with open(fname, 'r') as fid: lines = fid.readlines() ch_names, poss = list(), list() nasion = lpa = rpa = None for i, line in enumerate(lines): items = line.split() if not items: continue elif len(items) != 5: raise ValueError( "Error reading %s, line %s has unexpected number of entries:\n" "%s" % (fname, i, line.rstrip())) num = items[1] if num == '67': continue # centroid pos = np.array([float(item) for item in items[2:]]) if num == '78': nasion = pos elif num == '76': lpa = pos elif num == '82': rpa = pos else: ch_names.append(items[0]) poss.append(pos) electrodes = _check_dupes_odict(ch_names, poss) return make_dig_montage(electrodes, nasion, lpa, rpa) def read_dig_fif(fname): r"""Read digitized points from a .fif file. Note that electrode names are not present in the .fif file so they are here defined with the convention from VectorView systems (EEG001, EEG002, etc.) Parameters ---------- fname : path-like FIF file from which to read digitization locations. Returns ------- montage : instance of DigMontage The montage. See Also -------- DigMontage read_dig_dat read_dig_egi read_dig_captrak read_dig_polhemus_isotrak read_dig_hpts read_dig_localite make_dig_montage """ _check_fname(fname, overwrite='read', must_exist=True) # Load the dig data f, tree = fiff_open(fname)[:2] with f as fid: dig = _read_dig_fif(fid, tree) ch_names = [] for d in dig: if d['kind'] == FIFF.FIFFV_POINT_EEG: ch_names.append('EEG%03d' % d['ident']) montage = DigMontage(dig=dig, ch_names=ch_names) return montage def read_dig_hpts(fname, unit='mm'): """Read historical .hpts mne-c files. Parameters ---------- fname : path-like The filepath of .hpts file. unit : 'm' | 'cm' | 'mm' Unit of the positions. Defaults to 'mm'. Returns ------- montage : instance of DigMontage The montage. See Also -------- DigMontage read_dig_captrak read_dig_dat read_dig_egi read_dig_fif read_dig_localite read_dig_polhemus_isotrak make_dig_montage Notes ----- The hpts format digitzer data file may contain comment lines starting with the pound sign (#) and data lines of the form:: <*category*> <*identifier*> <*x/mm*> <*y/mm*> <*z/mm*> where: ``<*category*>`` defines the type of points. Allowed categories are: ``hpi``, ``cardinal`` (fiducial), ``eeg``, and ``extra`` corresponding to head-position indicator coil locations, cardinal landmarks, EEG electrode locations, and additional head surface points, respectively. ``<*identifier*>`` identifies the point. The identifiers are usually sequential numbers. For cardinal landmarks, 1 = left auricular point, 2 = nasion, and 3 = right auricular point. For EEG electrodes, identifier = 0 signifies the reference electrode. ``<*x/mm*> , <*y/mm*> , <*z/mm*>`` Location of the point, usually in the head coordinate system in millimeters. If your points are in [m] then unit parameter can be changed. For example:: cardinal 2 -5.6729 -12.3873 -30.3671 cardinal 1 -37.6782 -10.4957 91.5228 cardinal 3 -131.3127 9.3976 -22.2363 hpi 1 -30.4493 -11.8450 83.3601 hpi 2 -122.5353 9.2232 -28.6828 hpi 3 -6.8518 -47.0697 -37.0829 hpi 4 7.3744 -50.6297 -12.1376 hpi 5 -33.4264 -43.7352 -57.7756 eeg FP1 3.8676 -77.0439 -13.0212 eeg FP2 -31.9297 -70.6852 -57.4881 eeg F7 -6.1042 -68.2969 45.4939 ... """ from ._standard_montage_utils import _str_names, _str fname = _check_fname(fname, overwrite='read', must_exist=True) _scale = _check_unit_and_get_scaling(unit) out = np.genfromtxt(fname, comments='#', dtype=(_str, _str, 'f8', 'f8', 'f8')) kind, label = _str_names(out['f0']), _str_names(out['f1']) kind = [k.lower() for k in kind] xyz = np.array([out['f%d' % ii] for ii in range(2, 5)]).T xyz *= _scale del _scale fid_idx_to_label = {'1': 'lpa', '2': 'nasion', '3': 'rpa'} fid = {fid_idx_to_label[label[ii]]: this_xyz for ii, this_xyz in enumerate(xyz) if kind[ii] == 'cardinal'} ch_pos = {label[ii]: this_xyz for ii, this_xyz in enumerate(xyz) if kind[ii] == 'eeg'} hpi = np.array([this_xyz for ii, this_xyz in enumerate(xyz) if kind[ii] == 'hpi']) hpi.shape = (-1, 3) # in case it's empty hsp = np.array([this_xyz for ii, this_xyz in enumerate(xyz) if kind[ii] == 'extra']) hsp.shape = (-1, 3) # in case it's empty return make_dig_montage(ch_pos=ch_pos, **fid, hpi=hpi, hsp=hsp) def read_dig_egi(fname): """Read electrode locations from EGI system. Parameters ---------- fname : path-like EGI MFF XML coordinates file from which to read digitization locations. Returns ------- montage : instance of DigMontage The montage. See Also -------- DigMontage read_dig_captrak read_dig_dat read_dig_fif read_dig_hpts read_dig_localite read_dig_polhemus_isotrak make_dig_montage """ _check_fname(fname, overwrite='read', must_exist=True) data = _read_dig_montage_egi( fname=fname, _scaling=1., _all_data_kwargs_are_none=True ) return make_dig_montage(**data) def read_dig_captrak(fname): """Read electrode locations from CapTrak Brain Products system. Parameters ---------- fname : path-like BrainVision CapTrak coordinates file from which to read EEG electrode locations. This is typically in XML format with the .bvct extension. Returns ------- montage : instance of DigMontage The montage. See Also -------- DigMontage read_dig_dat read_dig_egi read_dig_fif read_dig_hpts read_dig_localite read_dig_polhemus_isotrak make_dig_montage """ _check_fname(fname, overwrite='read', must_exist=True) data = _parse_brainvision_dig_montage(fname, scale=1e-3) return make_dig_montage(**data) def read_dig_localite(fname, nasion=None, lpa=None, rpa=None): """Read Localite .csv file. Parameters ---------- fname : path-like File name. nasion : str | None Name of nasion fiducial point. lpa : str | None Name of left preauricular fiducial point. rpa : str | None Name of right preauricular fiducial point. Returns ------- montage : instance of DigMontage The montage. See Also -------- DigMontage read_dig_captrak read_dig_dat read_dig_egi read_dig_fif read_dig_hpts read_dig_polhemus_isotrak make_dig_montage """ ch_pos = {} with open(fname) as f: f.readline() # skip first row for row in f: _, name, x, y, z = row.split(",") ch_pos[name] = np.array((float(x), float(y), float(z))) / 1000 if nasion is not None: nasion = ch_pos.pop(nasion) if lpa is not None: lpa = ch_pos.pop(lpa) if rpa is not None: rpa = ch_pos.pop(rpa) return make_dig_montage(ch_pos, nasion, lpa, rpa) def _get_montage_in_head(montage): coords = set([d['coord_frame'] for d in montage.dig]) montage = montage.copy() if len(coords) == 1 and coords.pop() == FIFF.FIFFV_COORD_HEAD: _ensure_fiducials_head(montage.dig) return montage else: return transform_to_head(montage) def _set_montage_fnirs(info, montage): """Set the montage for fNIRS data. This needs to be different to electrodes as each channel has three coordinates that need to be set. For each channel there is a source optode location, a detector optode location, and a channel midpoint that must be stored. This function modifies info['chs'][#]['loc'] and info['dig'] in place. """ from ..preprocessing.nirs import _validate_nirs_info # Validate that the fNIRS info is correctly formatted picks = _validate_nirs_info(info) # Modify info['chs'][#]['loc'] in place num_ficiduals = len(montage.dig) - len(montage.ch_names) for ch_idx in picks: ch = info['chs'][ch_idx]['ch_name'] source, detector = ch.split(' ')[0].split('_') source_pos = montage.dig[montage.ch_names.index(source) + num_ficiduals]['r'] detector_pos = montage.dig[montage.ch_names.index(detector) + num_ficiduals]['r'] info['chs'][ch_idx]['loc'][3:6] = source_pos info['chs'][ch_idx]['loc'][6:9] = detector_pos midpoint = (source_pos + detector_pos) / 2 info['chs'][ch_idx]['loc'][:3] = midpoint info['chs'][ch_idx]['coord_frame'] = FIFF.FIFFV_COORD_HEAD # Modify info['dig'] in place with info._unlock(): info['dig'] = montage.dig @fill_doc def _set_montage(info, montage, match_case=True, match_alias=False, on_missing='raise'): """Apply montage to data. With a DigMontage, this function will replace the digitizer info with the values specified for the particular montage. Usually, a montage is expected to contain the positions of all EEG electrodes and a warning is raised when this is not the case. Parameters ---------- %(info_not_none)s %(montage)s %(match_case)s %(match_alias)s %(on_missing_montage)s Notes ----- This function will change the info variable in place. """ _validate_type(montage, (DigMontage, None, str), 'montage') if montage is None: # Next line modifies info['dig'] in place with info._unlock(): info['dig'] = None for ch in info['chs']: # Next line modifies info['chs'][#]['loc'] in place ch['loc'] = np.full(12, np.nan) return if isinstance(montage, str): # load builtin montage _check_option( parameter='montage', value=montage, allowed_values=[m.name for m in _BUILTIN_STANDARD_MONTAGES] ) montage = make_standard_montage(montage) mnt_head = _get_montage_in_head(montage) del montage def _backcompat_value(pos, ref_pos): if any(np.isnan(pos)): return np.full(6, np.nan) else: return np.concatenate((pos, ref_pos)) # get the channels in the montage in head ch_pos = mnt_head._get_ch_pos() # only get the eeg, seeg, dbs, ecog channels picks = pick_types( info, meg=False, eeg=True, seeg=True, dbs=True, ecog=True, exclude=()) non_picks = np.setdiff1d(np.arange(info['nchan']), picks) # get the reference position from the loc[3:6] chs = [info['chs'][ii] for ii in picks] non_names = [info['chs'][ii]['ch_name'] for ii in non_picks] del picks ref_pos = [ch['loc'][3:6] for ch in chs] # keep reference location from EEG-like channels if they # already exist and are all the same. custom_eeg_ref_dig = False # Note: ref position is an empty list for fieldtrip data if ref_pos: if all([np.equal(ref_pos[0], pos).all() for pos in ref_pos]) \ and not np.equal(ref_pos[0], [0, 0, 0]).all(): eeg_ref_pos = ref_pos[0] # since we have an EEG reference position, we have # to add it into the info['dig'] as EEG000 custom_eeg_ref_dig = True if not custom_eeg_ref_dig: refs = set(ch_pos) & {'EEG000', 'REF'} assert len(refs) <= 1 eeg_ref_pos = np.zeros(3) if not refs else ch_pos.pop(refs.pop()) # This raises based on info being subset/superset of montage info_names = [ch['ch_name'] for ch in chs] dig_names = mnt_head._get_dig_names() ref_names = [None, 'EEG000', 'REF'] if match_case: info_names_use = info_names dig_names_use = dig_names non_names_use = non_names else: ch_pos_use = OrderedDict( (name.lower(), pos) for name, pos in ch_pos.items()) info_names_use = [name.lower() for name in info_names] dig_names_use = [name.lower() if name is not None else name for name in dig_names] non_names_use = [name.lower() for name in non_names] ref_names = [name.lower() if name is not None else name for name in ref_names] n_dup = len(ch_pos) - len(ch_pos_use) if n_dup: raise ValueError('Cannot use match_case=False as %s montage ' 'name(s) require case sensitivity' % n_dup) n_dup = len(info_names_use) - len(set(info_names_use)) if n_dup: raise ValueError('Cannot use match_case=False as %s channel ' 'name(s) require case sensitivity' % n_dup) ch_pos = ch_pos_use del ch_pos_use del dig_names # use lookup table to match unrecognized channel names to known aliases if match_alias: alias_dict = (match_alias if isinstance(match_alias, dict) else CHANNEL_LOC_ALIASES) if not match_case: alias_dict = { ch_name.lower(): ch_alias.lower() for ch_name, ch_alias in alias_dict.items() } # excluded ch_alias not in info, to prevent unnecessary mapping and # warning messages based on aliases. alias_dict = { ch_name: ch_alias for ch_name, ch_alias in alias_dict.items() } info_names_use = [ alias_dict.get(ch_name, ch_name) for ch_name in info_names_use ] non_names_use = [ alias_dict.get(ch_name, ch_name) for ch_name in non_names_use ] # warn user if there is not a full overlap of montage with info_chs missing = np.where([use not in ch_pos for use in info_names_use])[0] if len(missing): # DigMontage is subset of info missing_names = [info_names[ii] for ii in missing] missing_coord_msg = ( 'DigMontage is only a subset of info. There are ' f'{len(missing)} channel position{_pl(missing)} ' 'not present in the DigMontage. The required channels are:\n\n' f'{missing_names}.\n\nConsider using inst.set_channel_types ' 'if these are not EEG channels, or use the on_missing ' 'parameter if the channel positions are allowed to be unknown ' 'in your analyses.' ) _on_missing(on_missing, missing_coord_msg) # set ch coordinates and names from digmontage or nan coords for ii in missing: ch_pos[info_names_use[ii]] = [np.nan] * 3 del info_names assert len(non_names_use) == len(non_names) # There are no issues here with fNIRS being in non_names_use because # these names are like "D1_S1_760" and the ch_pos for a fNIRS montage # will have entries "D1" and "S1". extra = np.where([non in ch_pos for non in non_names_use])[0] if len(extra): types = '/'.join(sorted(set( channel_type(info, non_picks[ii]) for ii in extra))) names = [non_names[ii] for ii in extra] warn(f'Not setting position{_pl(extra)} of {len(extra)} {types} ' f'channel{_pl(extra)} found in montage:\n{names}\n' 'Consider setting the channel types to be of ' f'{_docdict["montage_types"]} ' 'using inst.set_channel_types before calling inst.set_montage, ' 'or omit these channels when creating your montage.') for ch, use in zip(chs, info_names_use): # Next line modifies info['chs'][#]['loc'] in place if use in ch_pos: ch['loc'][:6] = _backcompat_value(ch_pos[use], eeg_ref_pos) ch['coord_frame'] = FIFF.FIFFV_COORD_HEAD del ch_pos # XXX this is probably wrong as it uses the order from the montage # rather than the order of our info['ch_names'] ... digpoints = [ mnt_head.dig[ii] for ii, name in enumerate(dig_names_use) if name in (info_names_use + ref_names)] # get a copy of the old dig if info['dig'] is not None: old_dig = info['dig'].copy() else: old_dig = [] # determine if needed to add an extra EEG REF DigPoint if custom_eeg_ref_dig: # ref_name = 'EEG000' if match_case else 'eeg000' ref_dig_dict = {'kind': FIFF.FIFFV_POINT_EEG, 'r': eeg_ref_pos, 'ident': 0, 'coord_frame': info['dig'].pop()['coord_frame']} ref_dig_point = _format_dig_points([ref_dig_dict])[0] # only append the reference dig point if it was already # in the old dig if ref_dig_point in old_dig: digpoints.append(ref_dig_point) # Next line modifies info['dig'] in place with info._unlock(): info['dig'] = _format_dig_points(digpoints, enforce_order=True) del digpoints # TODO: Ideally we would have a check like this, but read_raw_bids for ECoG # allows for a montage to be set without any fiducials, then silently the # info['dig'] can end up in the MNI_TAL frame... only because in our # conversion code, UNKNOWN is treated differently from any other frame # (e.g., MNI_TAL). We should clean this up at some point... # missing_fids = sum( # d['kind'] == FIFF.FIFFV_POINT_CARDINAL for d in info['dig'][:3]) != 3 # if missing_fids: # raise RuntimeError( # 'Could not find all three fiducials in the montage, this should ' # 'not happen. Please contact MNE-Python developers.') # Handle fNIRS with source, detector and channel fnirs_picks = _picks_to_idx(info, 'fnirs', allow_empty=True) if len(fnirs_picks) > 0: _set_montage_fnirs(info, mnt_head) def _read_isotrak_elp_points(fname): """Read Polhemus Isotrak digitizer data from a ``.elp`` file. Parameters ---------- fname : str The filepath of .elp Polhemus Isotrak file. Returns ------- out : dict of arrays The dictionary containing locations for 'nasion', 'lpa', 'rpa' and 'points'. """ value_pattern = r"\-?\d+\.?\d*e?\-?\d*" coord_pattern = r"({0})\s+({0})\s+({0})\s*$".format(value_pattern) with open(fname) as fid: file_str = fid.read() points_str = [m.groups() for m in re.finditer(coord_pattern, file_str, re.MULTILINE)] points = np.array(points_str, dtype=float) return { 'nasion': points[0], 'lpa': points[1], 'rpa': points[2], 'points': points[3:] } def _read_isotrak_hsp_points(fname): """Read Polhemus Isotrak digitizer data from a ``.hsp`` file. Parameters ---------- fname : str The filepath of .hsp Polhemus Isotrak file. Returns ------- out : dict of arrays The dictionary containing locations for 'nasion', 'lpa', 'rpa' and 'points'. """ def get_hsp_fiducial(line): return np.fromstring(line.replace('%F', ''), dtype=float, sep='\t') with open(fname) as ff: for line in ff: if 'position of fiducials' in line.lower(): break nasion = get_hsp_fiducial(ff.readline()) lpa = get_hsp_fiducial(ff.readline()) rpa = get_hsp_fiducial(ff.readline()) _ = ff.readline() line = ff.readline() if line: n_points, n_cols = np.fromstring(line, dtype=int, sep='\t') points = np.fromstring( string=ff.read(), dtype=float, sep='\t', ).reshape(-1, n_cols) assert points.shape[0] == n_points else: points = np.empty((0, 3)) return { 'nasion': nasion, 'lpa': lpa, 'rpa': rpa, 'points': points } def read_dig_polhemus_isotrak(fname, ch_names=None, unit='m'): """Read Polhemus digitizer data from a file. Parameters ---------- fname : path-like The filepath of Polhemus ISOTrak formatted file. File extension is expected to be '.hsp', '.elp' or '.eeg'. ch_names : None | list of str The names of the points. This will make the points considered as EEG channels. If None, channels will be assumed to be HPI if the extension is ``'.elp'``, and extra headshape points otherwise. unit : 'm' | 'cm' | 'mm' Unit of the digitizer file. Polhemus ISOTrak systems data is usually exported in meters. Defaults to 'm'. Returns ------- montage : instance of DigMontage The montage. See Also -------- DigMontage make_dig_montage read_polhemus_fastscan read_dig_captrak read_dig_dat read_dig_egi read_dig_fif read_dig_localite """ VALID_FILE_EXT = ('.hsp', '.elp', '.eeg') fname = _check_fname(fname, overwrite='read', must_exist=True) _scale = _check_unit_and_get_scaling(unit) _, ext = op.splitext(fname) _check_option('fname', ext, VALID_FILE_EXT) if ext == '.elp': data = _read_isotrak_elp_points(fname) else: # Default case we read points as hsp since is the most likely scenario data = _read_isotrak_hsp_points(fname) if _scale != 1: data = {key: val * _scale for key, val in data.items()} else: pass # noqa if ch_names is None: keyword = 'hpi' if ext == '.elp' else 'hsp' data[keyword] = data.pop('points') else: points = data.pop('points') if points.shape[0] == len(ch_names): data['ch_pos'] = OrderedDict(zip(ch_names, points)) else: raise ValueError(( "Length of ``ch_names`` does not match the number of points" " in {fname}. Expected ``ch_names`` length {n_points:d}," " given {n_chnames:d}" ).format( fname=fname, n_points=points.shape[0], n_chnames=len(ch_names) )) return make_dig_montage(**data) def _is_polhemus_fastscan(fname): header = '' with open(fname, 'r') as fid: for line in fid: if not line.startswith('%'): break header += line return 'FastSCAN' in header @verbose def read_polhemus_fastscan(fname, unit='mm', on_header_missing='raise', *, verbose=None): """Read Polhemus FastSCAN digitizer data from a ``.txt`` file. Parameters ---------- fname : path-like The path of .txt Polhemus FastSCAN file. unit : 'm' | 'cm' | 'mm' Unit of the digitizer file. Polhemus FastSCAN systems data is usually exported in millimeters. Defaults to 'mm'. %(on_header_missing)s %(verbose)s Returns ------- points : array, shape (n_points, 3) The digitization points in digitizer coordinates. See Also -------- read_dig_polhemus_isotrak make_dig_montage """ VALID_FILE_EXT = ['.txt'] fname = _check_fname(fname, overwrite='read', must_exist=True) _scale = _check_unit_and_get_scaling(unit) _, ext = op.splitext(fname) _check_option('fname', ext, VALID_FILE_EXT) if not _is_polhemus_fastscan(fname): msg = "%s does not contain a valid Polhemus FastSCAN header" % fname _on_missing(on_header_missing, msg) points = _scale * np.loadtxt(fname, comments='%', ndmin=2) _check_dig_shape(points) return points def _read_eeglab_locations(fname): ch_names = np.genfromtxt(fname, dtype=str, usecols=3).tolist() topo = np.loadtxt(fname, dtype=float, usecols=[1, 2]) sph = _topo_to_sph(topo) pos = _sph_to_cart(sph) pos[:, [0, 1]] = pos[:, [1, 0]] * [-1, 1] return ch_names, pos def read_custom_montage(fname, head_size=HEAD_SIZE_DEFAULT, coord_frame=None): """Read a montage from a file. Parameters ---------- fname : path-like File extension is expected to be: '.loc' or '.locs' or '.eloc' (for EEGLAB files), '.sfp' (BESA/EGI files), '.csd', '.elc', '.txt', '.csd', '.elp' (BESA spherical), '.bvef' (BrainVision files), '.csv', '.tsv', '.xyz' (XYZ coordinates). head_size : float | None The size of the head (radius, in [m]). If ``None``, returns the values read from the montage file with no modification. Defaults to 0.095m. coord_frame : str | None The coordinate frame of the points. Usually this is "unknown" for native digitizer space. Defaults to None, which is "unknown" for most readers but "head" for EEGLAB. .. versionadded:: 0.20 Returns ------- montage : instance of DigMontage The montage. See Also -------- make_dig_montage make_standard_montage Notes ----- The function is a helper to read electrode positions you may have in various formats. Most of these format are weakly specified in terms of units, coordinate systems. It implies that setting a montage using a DigMontage produced by this function may be problematic. If you use a standard/template (eg. 10/20, 10/10 or 10/05) we recommend you use :func:`make_standard_montage`. If you can have positions in memory you can also use :func:`make_dig_montage` that takes arrays as input. """ from ._standard_montage_utils import ( _read_theta_phi_in_degrees, _read_sfp, _read_csd, _read_elc, _read_elp_besa, _read_brainvision, _read_xyz ) SUPPORTED_FILE_EXT = { 'eeglab': ('.loc', '.locs', '.eloc', ), 'hydrocel': ('.sfp', ), 'matlab': ('.csd', ), 'asa electrode': ('.elc', ), 'generic (Theta-phi in degrees)': ('.txt', ), 'standard BESA spherical': ('.elp', ), # NB: not same as polhemus elp 'brainvision': ('.bvef', ), 'xyz': ('.csv', '.tsv', '.xyz'), } fname = _check_fname(fname, overwrite='read', must_exist=True) _, ext = op.splitext(fname) _check_option('fname', ext, list(sum(SUPPORTED_FILE_EXT.values(), ()))) if ext in SUPPORTED_FILE_EXT['eeglab']: if head_size is None: raise ValueError( "``head_size`` cannot be None for '{}'".format(ext)) ch_names, pos = _read_eeglab_locations(fname) scale = head_size / np.median(np.linalg.norm(pos, axis=-1)) pos *= scale montage = make_dig_montage( ch_pos=OrderedDict(zip(ch_names, pos)), coord_frame='head', ) elif ext in SUPPORTED_FILE_EXT['hydrocel']: montage = _read_sfp(fname, head_size=head_size) elif ext in SUPPORTED_FILE_EXT['matlab']: montage = _read_csd(fname, head_size=head_size) elif ext in SUPPORTED_FILE_EXT['asa electrode']: montage = _read_elc(fname, head_size=head_size) elif ext in SUPPORTED_FILE_EXT['generic (Theta-phi in degrees)']: if head_size is None: raise ValueError( "``head_size`` cannot be None for '{}'".format(ext)) montage = _read_theta_phi_in_degrees(fname, head_size=head_size, fid_names=('Nz', 'LPA', 'RPA')) elif ext in SUPPORTED_FILE_EXT['standard BESA spherical']: montage = _read_elp_besa(fname, head_size) elif ext in SUPPORTED_FILE_EXT['brainvision']: montage = _read_brainvision(fname, head_size) elif ext in SUPPORTED_FILE_EXT['xyz']: montage = _read_xyz(fname) if coord_frame is not None: coord_frame = _coord_frame_const(coord_frame) for d in montage.dig: d['coord_frame'] = coord_frame return montage def compute_dev_head_t(montage): """Compute device to head transform from a DigMontage. Parameters ---------- montage : DigMontage The `~mne.channels.DigMontage` must contain the fiducials in head coordinate system and hpi points in both head and meg device coordinate system. Returns ------- dev_head_t : Transform A Device-to-Head transformation matrix. """ _, coord_frame = _get_fid_coords(montage.dig) if coord_frame != FIFF.FIFFV_COORD_HEAD: raise ValueError('montage should have been set to head coordinate ' 'system with transform_to_head function.') hpi_head = np.array( [d['r'] for d in montage.dig if (d['kind'] == FIFF.FIFFV_POINT_HPI and d['coord_frame'] == FIFF.FIFFV_COORD_HEAD)], float) hpi_dev = np.array( [d['r'] for d in montage.dig if (d['kind'] == FIFF.FIFFV_POINT_HPI and d['coord_frame'] == FIFF.FIFFV_COORD_DEVICE)], float) if not (len(hpi_head) == len(hpi_dev) and len(hpi_dev) > 0): raise ValueError(( "To compute Device-to-Head transformation, the same number of HPI" " points in device and head coordinates is required. (Got {dev}" " points in device and {head} points in head coordinate systems)" ).format(dev=len(hpi_dev), head=len(hpi_head))) trans = _quat_to_affine(_fit_matched_points(hpi_dev, hpi_head)[0]) return Transform(fro='meg', to='head', trans=trans) @verbose def compute_native_head_t(montage, *, on_missing='warn', verbose=None): """Compute the native-to-head transformation for a montage. This uses the fiducials in the native space to transform to compute the transform to the head coordinate frame. Parameters ---------- montage : instance of DigMontage The montage. %(on_missing_fiducials)s .. versionadded:: 1.2 %(verbose)s Returns ------- native_head_t : instance of Transform A native-to-head transformation matrix. """ # Get fiducial points and their coord_frame fid_coords, coord_frame = _get_fid_coords(montage.dig, raise_error=False) if coord_frame is None: coord_frame = FIFF.FIFFV_COORD_UNKNOWN if coord_frame == FIFF.FIFFV_COORD_HEAD: native_head_t = np.eye(4) else: fid_keys = ('nasion', 'lpa', 'rpa') for key in fid_keys: if fid_coords[key] is None: msg = ( f'Fiducial point {key} not found, assuming identity ' f'{_verbose_frames[coord_frame]} to head transformation') _on_missing(on_missing, msg, error_klass=RuntimeError) native_head_t = np.eye(4) break else: native_head_t = get_ras_to_neuromag_trans( *[fid_coords[key] for key in fid_keys]) return Transform(coord_frame, 'head', native_head_t) def make_standard_montage(kind, head_size='auto'): """Read a generic (built-in) standard montage that ships with MNE-Python. Parameters ---------- kind : str The name of the montage to use. .. note:: You can retrieve the names of all built-in montages via :func:`mne.channels.get_builtin_montages`. head_size : float | None | str The head size (radius, in meters) to use for spherical montages. Can be None to not scale the read sizes. ``'auto'`` (default) will use 95mm for all montages except the ``'standard*'``, ``'mgh*'``, and ``'artinis*'``, which are already in fsaverage's MRI coordinates (same as MNI). Returns ------- montage : instance of DigMontage The montage. See Also -------- get_builtin_montages make_dig_montage read_custom_montage Notes ----- Individualized (digitized) electrode positions should be read in using :func:`read_dig_captrak`, :func:`read_dig_dat`, :func:`read_dig_egi`, :func:`read_dig_fif`, :func:`read_dig_polhemus_isotrak`, :func:`read_dig_hpts`, or manually made with :func:`make_dig_montage`. .. versionadded:: 0.19.0 """ from ._standard_montage_utils import standard_montage_look_up_table _validate_type(kind, str, 'kind') _check_option( parameter='kind', value=kind, allowed_values=[m.name for m in _BUILTIN_STANDARD_MONTAGES] ) _validate_type(head_size, ('numeric', str, None), 'head_size') if isinstance(head_size, str): _check_option('head_size', head_size, ('auto',), extra='when str') if kind.startswith(('standard', 'mgh', 'artinis')): head_size = None else: head_size = HEAD_SIZE_DEFAULT return standard_montage_look_up_table[kind](head_size=head_size) def _check_dig_shape(pts): _validate_type(pts, np.ndarray, 'points') if pts.ndim != 2 or pts.shape[-1] != 3: raise ValueError( f'Points must be of shape (n, 3) instead of {pts.shape}')