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tyler
2025-08-19 09:13:22 -07:00
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commit 0977a3e14d
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mne/channels/__init__.py
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"""Module dedicated to manipulation of channels.
Can be used for setting of sensor locations used for processing and plotting.
"""
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import lazy_loader as lazy
(__getattr__, __dir__, __all__) = lazy.attach_stub(__name__, __file__)
mne/channels/__init__.pyi
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__all__ = [
"DigMontage",
"Layout",
"_EEG_SELECTIONS",
"_SELECTIONS",
"_divide_to_regions",
"combine_channels",
"compute_dev_head_t",
"compute_native_head_t",
"equalize_channels",
"find_ch_adjacency",
"find_layout",
"fix_mag_coil_types",
"generate_2d_layout",
"get_builtin_ch_adjacencies",
"get_builtin_montages",
"make_1020_channel_selections",
"make_dig_montage",
"make_eeg_layout",
"make_grid_layout",
"make_standard_montage",
"read_ch_adjacency",
"read_custom_montage",
"read_dig_captrak",
"read_dig_dat",
"read_dig_egi",
"read_dig_fif",
"read_dig_hpts",
"read_dig_localite",
"read_dig_polhemus_isotrak",
"read_layout",
"read_polhemus_fastscan",
"read_vectorview_selection",
"rename_channels",
"transform_to_head",
"unify_bad_channels",
]
from .channels import (
_EEG_SELECTIONS,
_SELECTIONS,
_divide_to_regions,
combine_channels,
equalize_channels,
find_ch_adjacency,
fix_mag_coil_types,
get_builtin_ch_adjacencies,
make_1020_channel_selections,
read_ch_adjacency,
read_vectorview_selection,
rename_channels,
unify_bad_channels,
)
from .layout import (
Layout,
find_layout,
generate_2d_layout,
make_eeg_layout,
make_grid_layout,
read_layout,
)
from .montage import (
DigMontage,
compute_dev_head_t,
compute_native_head_t,
get_builtin_montages,
make_dig_montage,
make_standard_montage,
read_custom_montage,
read_dig_captrak,
read_dig_dat,
read_dig_egi,
read_dig_fif,
read_dig_hpts,
read_dig_localite,
read_dig_polhemus_isotrak,
read_polhemus_fastscan,
transform_to_head,
)
mne/channels/_dig_montage_utils.py
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
from ..utils import Bunch, _check_fname, _soft_import, warn
def _read_dig_montage_egi(
fname,
_scaling,
_all_data_kwargs_are_none,
):
if not _all_data_kwargs_are_none:
raise ValueError(
"hsp, hpi, elp, point_names, fif must all be None if egi is not None"
)
_check_fname(fname, overwrite="read", must_exist=True)
defusedxml = _soft_import("defusedxml", "reading EGI montages")
root = defusedxml.ElementTree.parse(fname).getroot()
ns = root.tag[root.tag.index("{") : root.tag.index("}") + 1]
sensors = root.find(f"{ns}sensorLayout/{ns}sensors")
fids = dict()
dig_ch_pos = dict()
fid_name_map = {
"Nasion": "nasion",
"Right periauricular point": "rpa",
"Left periauricular point": "lpa",
}
for s in sensors:
name, number, kind = s[0].text, int(s[1].text), int(s[2].text)
coordinates = np.array([float(s[3].text), float(s[4].text), float(s[5].text)])
coordinates *= _scaling
# EEG Channels
if kind == 0:
dig_ch_pos[f"EEG {number:03d}"] = coordinates
# Reference
elif kind == 1:
dig_ch_pos[f"EEG {len(dig_ch_pos) + 1:03d}"] = coordinates
# Fiducials
elif kind == 2:
fid_name = fid_name_map[name]
fids[fid_name] = coordinates
# Unknown
else:
warn(
f"Unknown sensor type {kind} detected. Skipping sensor..."
"Proceed with caution!"
)
return Bunch(
# EGI stuff
nasion=fids["nasion"],
lpa=fids["lpa"],
rpa=fids["rpa"],
ch_pos=dig_ch_pos,
coord_frame="unknown",
)
def _parse_brainvision_dig_montage(fname, scale):
FID_NAME_MAP = {"Nasion": "nasion", "RPA": "rpa", "LPA": "lpa"}
defusedxml = _soft_import("defusedxml", "reading BrainVision montages")
root = defusedxml.ElementTree.parse(fname).getroot()
sensors = root.find("CapTrakElectrodeList")
fids, dig_ch_pos = dict(), dict()
for s in sensors:
name = s.find("Name").text
is_fid = name in FID_NAME_MAP
coordinates = scale * np.array(
[float(s.find("X").text), float(s.find("Y").text), float(s.find("Z").text)]
)
# Fiducials
if is_fid:
fids[FID_NAME_MAP[name]] = coordinates
# EEG Channels
else:
dig_ch_pos[name] = coordinates
return dict(
# BVCT stuff
nasion=fids["nasion"],
lpa=fids["lpa"],
rpa=fids["rpa"],
ch_pos=dig_ch_pos,
coord_frame="unknown",
)
mne/channels/_standard_montage_utils.py
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import csv
import os.path as op
from collections import OrderedDict
from functools import partial
import numpy as np
from .._freesurfer import get_mni_fiducials
from ..transforms import _sph_to_cart
from ..utils import _pl, _soft_import, warn
from . import __file__ as _CHANNELS_INIT_FILE
from .montage import make_dig_montage
MONTAGE_PATH = op.join(op.dirname(_CHANNELS_INIT_FILE), "data", "montages")
_str = "U100"
# In standard_1020, T9=LPA, T10=RPA, Nasion is the same as Iz with a
# sign-flipped Y value
def _egi_256(head_size):
fname = op.join(MONTAGE_PATH, "EGI_256.csd")
montage = _read_csd(fname, head_size)
ch_pos = montage._get_ch_pos()
# For this cap, the Nasion is the frontmost electrode,
# LPA/RPA we approximate by putting 75% of the way (toward the front)
# between the two electrodes that are halfway down the ear holes
nasion = ch_pos["E31"]
lpa = 0.75 * ch_pos["E67"] + 0.25 * ch_pos["E94"]
rpa = 0.75 * ch_pos["E219"] + 0.25 * ch_pos["E190"]
fids_montage = make_dig_montage(
coord_frame="unknown",
nasion=nasion,
lpa=lpa,
rpa=rpa,
)
montage += fids_montage # add fiducials to montage
return montage
def _easycap(basename, head_size):
fname = op.join(MONTAGE_PATH, basename)
montage = _read_theta_phi_in_degrees(fname, head_size, add_fiducials=True)
return montage
def _hydrocel(basename, head_size):
fname = op.join(MONTAGE_PATH, basename)
return _read_sfp(fname, head_size)
def _str_names(ch_names):
return [str(ch_name) for ch_name in ch_names]
def _safe_np_loadtxt(fname, **kwargs):
out = np.genfromtxt(fname, **kwargs)
ch_names = _str_names(out["f0"])
others = tuple(out[f"f{ii}"] for ii in range(1, len(out.dtype.fields)))
return (ch_names,) + others
def _biosemi(basename, head_size):
fname = op.join(MONTAGE_PATH, basename)
fid_names = ("Nz", "LPA", "RPA")
return _read_theta_phi_in_degrees(fname, head_size, fid_names)
def _mgh_or_standard(basename, head_size, coord_frame="unknown"):
fid_names = ("Nz", "LPA", "RPA")
fname = op.join(MONTAGE_PATH, basename)
ch_names_, pos = [], []
with open(fname) as fid:
# Ignore units as we will scale later using the norms anyway
for line in fid:
if "Positions\n" in line:
break
pos = []
for line in fid:
if "Labels\n" in line:
break
pos.append(list(map(float, line.split())))
for line in fid:
if not line or not set(line) - {" "}:
break
ch_names_.append(line.strip(" ").strip("\n"))
pos = np.array(pos) / 1000.0
ch_pos = _check_dupes_odict(ch_names_, pos)
nasion, lpa, rpa = (ch_pos.pop(n) for n in fid_names)
if head_size is None:
scale = 1.0
else:
scale = head_size / np.median(np.linalg.norm(pos, axis=1))
for value in ch_pos.values():
value *= scale
# if we are in MRI/MNI coordinates, we need to replace nasion, LPA, and RPA
# with those of fsaverage for ``trans='fsaverage'`` to work
if coord_frame == "mri":
lpa, nasion, rpa = (x["r"].copy() for x in get_mni_fiducials("fsaverage"))
nasion *= scale
lpa *= scale
rpa *= scale
return make_dig_montage(
ch_pos=ch_pos, coord_frame=coord_frame, nasion=nasion, lpa=lpa, rpa=rpa
)
standard_montage_look_up_table = {
"EGI_256": _egi_256,
"easycap-M1": partial(_easycap, basename="easycap-M1.txt"),
"easycap-M10": partial(_easycap, basename="easycap-M10.txt"),
"easycap-M43": partial(_easycap, basename="easycap-M43.txt"),
"GSN-HydroCel-128": partial(_hydrocel, basename="GSN-HydroCel-128.sfp"),
"GSN-HydroCel-129": partial(_hydrocel, basename="GSN-HydroCel-129.sfp"),
"GSN-HydroCel-256": partial(_hydrocel, basename="GSN-HydroCel-256.sfp"),
"GSN-HydroCel-257": partial(_hydrocel, basename="GSN-HydroCel-257.sfp"),
"GSN-HydroCel-32": partial(_hydrocel, basename="GSN-HydroCel-32.sfp"),
"GSN-HydroCel-64_1.0": partial(_hydrocel, basename="GSN-HydroCel-64_1.0.sfp"),
"GSN-HydroCel-65_1.0": partial(_hydrocel, basename="GSN-HydroCel-65_1.0.sfp"),
"biosemi128": partial(_biosemi, basename="biosemi128.txt"),
"biosemi16": partial(_biosemi, basename="biosemi16.txt"),
"biosemi160": partial(_biosemi, basename="biosemi160.txt"),
"biosemi256": partial(_biosemi, basename="biosemi256.txt"),
"biosemi32": partial(_biosemi, basename="biosemi32.txt"),
"biosemi64": partial(_biosemi, basename="biosemi64.txt"),
"mgh60": partial(_mgh_or_standard, basename="mgh60.elc", coord_frame="mri"),
"mgh70": partial(_mgh_or_standard, basename="mgh70.elc", coord_frame="mri"),
"standard_1005": partial(
_mgh_or_standard, basename="standard_1005.elc", coord_frame="mri"
),
"standard_1020": partial(
_mgh_or_standard, basename="standard_1020.elc", coord_frame="mri"
),
"standard_alphabetic": partial(
_mgh_or_standard, basename="standard_alphabetic.elc", coord_frame="mri"
),
"standard_postfixed": partial(
_mgh_or_standard, basename="standard_postfixed.elc", coord_frame="mri"
),
"standard_prefixed": partial(
_mgh_or_standard, basename="standard_prefixed.elc", coord_frame="mri"
),
"standard_primed": partial(
_mgh_or_standard, basename="standard_primed.elc", coord_frame="mri"
),
"artinis-octamon": partial(
_mgh_or_standard, coord_frame="mri", basename="artinis-octamon.elc"
),
"artinis-brite23": partial(
_mgh_or_standard, coord_frame="mri", basename="artinis-brite23.elc"
),
"brainproducts-RNP-BA-128": partial(
_easycap, basename="brainproducts-RNP-BA-128.txt"
),
}
def _read_sfp(fname, head_size):
"""Read .sfp BESA/EGI files."""
# fname has been already checked
fid_names = ("FidNz", "FidT9", "FidT10")
options = dict(dtype=(_str, "f4", "f4", "f4"))
ch_names, xs, ys, zs = _safe_np_loadtxt(fname, **options)
# deal with "headshape"
mask = np.array([ch_name == "headshape" for ch_name in ch_names], bool)
hsp = np.stack([xs[mask], ys[mask], zs[mask]], axis=-1)
mask = ~mask
pos = np.stack([xs[mask], ys[mask], zs[mask]], axis=-1)
ch_names = [ch_name for ch_name, m in zip(ch_names, mask) if m]
ch_pos = _check_dupes_odict(ch_names, pos)
del xs, ys, zs, ch_names
# no one grants that fid names are there.
nasion, lpa, rpa = (ch_pos.pop(n, None) for n in fid_names)
if head_size is not None:
scale = head_size / np.median(np.linalg.norm(pos, axis=-1))
for value in ch_pos.values():
value *= scale
nasion = nasion * scale if nasion is not None else None
lpa = lpa * scale if lpa is not None else None
rpa = rpa * scale if rpa is not None else None
return make_dig_montage(
ch_pos=ch_pos, coord_frame="unknown", nasion=nasion, rpa=rpa, lpa=lpa, hsp=hsp
)
def _read_csd(fname, head_size):
# Label, Theta, Phi, Radius, X, Y, Z, off sphere surface
options = dict(
comments="//", dtype=(_str, "f4", "f4", "f4", "f4", "f4", "f4", "f4")
)
ch_names, _, _, _, xs, ys, zs, _ = _safe_np_loadtxt(fname, **options)
pos = np.stack([xs, ys, zs], axis=-1)
if head_size is not None:
pos *= head_size / np.median(np.linalg.norm(pos, axis=1))
return make_dig_montage(ch_pos=_check_dupes_odict(ch_names, pos))
def _check_dupes_odict(ch_names, pos):
"""Warn if there are duplicates, then turn to ordered dict."""
ch_names = list(ch_names)
dups = OrderedDict((ch_name, ch_names.count(ch_name)) for ch_name in ch_names)
dups = OrderedDict((ch_name, count) for ch_name, count in dups.items() if count > 1)
n = len(dups)
if n:
dups = ", ".join(f"{ch_name} ({count})" for ch_name, count in dups.items())
warn(
f"Duplicate channel position{_pl(n)} found, the last will be "
f"used for {dups}"
)
return OrderedDict(zip(ch_names, pos))
def _read_elc(fname, head_size):
"""Read .elc files.
The `.elc` files are so-called "asa electrode files". ASA here stands for
Advances Source Analysis, and is a software package developed and sold by
the ANT Neuro company. They provide a device for sensor digitization, called
'xensor', which produces the `.elc` files.
Parameters
----------
fname : str
File extension is expected to be '.elc'.
head_size : float | None
The size of the head in [m]. If none, returns the values read from the
file with no modification.
Returns
-------
montage : instance of DigMontage
The montage units are [m].
"""
fid_names = ("Nz", "LPA", "RPA")
with open(fname) as fid:
# Read units
# _read_elc does require to detect the units. (see _mgh_or_standard)
for line in fid:
if "UnitPosition" in line:
units = line.split()[1]
scale = dict(m=1.0, mm=1e-3)[units]
break
else:
raise RuntimeError(f"Could not detect units in file {fname}")
for line in fid:
if "Positions\n" in line:
break
# Read positions
new_style = False
pos = []
for line in fid:
if "Labels\n" in line:
break
if ":" in line:
# Of the 'new' format: `E01 : 5.288 -3.658 119.693`
pos.append(list(map(float, line.split(":")[1].split())))
new_style = True
else:
# Of the 'old' format: `5.288 -3.658 119.693`
pos.append(list(map(float, line.split())))
# Read labels
ch_names_ = []
for line in fid:
if not line or not set(line) - {" "}:
break
if new_style:
# Not sure how this format would deal with spaces in channel labels,
# but none of my test files had this, so let's wait until it comes up.
parsed = line.strip(" ").strip("\n").split()
else:
parsed = [line.strip(" ").strip("\n")]
ch_names_.extend(parsed)
pos = np.array(pos) * scale
if head_size is not None:
pos *= head_size / np.median(np.linalg.norm(pos, axis=1))
ch_pos = _check_dupes_odict(ch_names_, pos)
nasion, lpa, rpa = (ch_pos.pop(n, None) for n in fid_names)
return make_dig_montage(
ch_pos=ch_pos, coord_frame="unknown", nasion=nasion, lpa=lpa, rpa=rpa
)
def _read_theta_phi_in_degrees(fname, head_size, fid_names=None, add_fiducials=False):
ch_names, theta, phi = _safe_np_loadtxt(
fname, skip_header=1, dtype=(_str, "i4", "i4")
)
if add_fiducials:
# Add fiducials based on 10/20 spherical coordinate definitions
# http://chgd.umich.edu/wp-content/uploads/2014/06/
# 10-20_system_positioning.pdf
# extrapolated from other sensor coordinates in the Easycap layouts
# https://www.easycap.de/wp-content/uploads/2018/02/
# Easycap-Equidistant-Layouts.pdf
assert fid_names is None
fid_names = ["Nasion", "LPA", "RPA"]
ch_names.extend(fid_names)
theta = np.append(theta, [115, -115, 115])
phi = np.append(phi, [90, 0, 0])
radii = np.full(len(phi), head_size)
pos = _sph_to_cart(np.array([radii, np.deg2rad(phi), np.deg2rad(theta)]).T)
ch_pos = _check_dupes_odict(ch_names, pos)
nasion, lpa, rpa = None, None, None
if fid_names is not None:
nasion, lpa, rpa = (ch_pos.pop(n, None) for n in fid_names)
return make_dig_montage(
ch_pos=ch_pos, coord_frame="unknown", nasion=nasion, lpa=lpa, rpa=rpa
)
def _read_elp_besa(fname, head_size):
# This .elp is not the same as polhemus elp. see _read_isotrak_elp_points
dtype = np.dtype("S8, S8, f8, f8, f8")
data = np.loadtxt(fname, dtype=dtype)
ch_names = data["f1"].astype(str).tolist()
az = data["f2"]
horiz = data["f3"]
radius = np.abs(az / 180.0)
az = np.deg2rad(np.array([h if a >= 0.0 else 180 + h for h, a in zip(horiz, az)]))
pol = radius * np.pi
rad = data["f4"] / 100
pos = _sph_to_cart(np.array([rad, az, pol]).T)
if head_size is not None:
pos *= head_size / np.median(np.linalg.norm(pos, axis=1))
ch_pos = _check_dupes_odict(ch_names, pos)
fid_names = ("Nz", "LPA", "RPA")
# No one grants that the fid names actually exist.
nasion, lpa, rpa = (ch_pos.pop(n, None) for n in fid_names)
return make_dig_montage(ch_pos=ch_pos, nasion=nasion, lpa=lpa, rpa=rpa)
def _read_brainvision(fname, head_size):
# 'BrainVision Electrodes File' format
# Based on BrainVision Analyzer coordinate system: Defined between
# standard electrode positions: X-axis from T7 to T8, Y-axis from Oz to
# Fpz, Z-axis orthogonal from XY-plane through Cz, fit to a sphere if
# idealized (when radius=1), specified in millimeters
defusedxml = _soft_import("defusedxml", "reading BrainVision montages")
root = defusedxml.ElementTree.parse(fname).getroot()
ch_names = [s.text for s in root.findall("./Electrode/Name")]
theta = [float(s.text) for s in root.findall("./Electrode/Theta")]
pol = np.deg2rad(np.array(theta))
phi = [float(s.text) for s in root.findall("./Electrode/Phi")]
az = np.deg2rad(np.array(phi))
rad = [float(s.text) for s in root.findall("./Electrode/Radius")]
rad = np.array(rad) # specified in mm
pos = _sph_to_cart(np.array([rad, az, pol]).T)
if head_size is not None:
pos *= head_size / np.median(np.linalg.norm(pos, axis=1))
return make_dig_montage(ch_pos=_check_dupes_odict(ch_names, pos))
def _read_xyz(fname):
"""Import EEG channel locations from CSV, TSV, or XYZ files.
CSV and TSV files should have columns 4 columns containing
ch_name, x, y, and z. Each row represents one channel.
XYZ files should have 5 columns containing
count, x, y, z, and ch_name. Each row represents one channel
CSV files should be separated by commas, TSV and XYZ files should be
separated by tabs.
Parameters
----------
fname : str
Name of the file to read channel locations from.
Returns
-------
montage : instance of DigMontage
The montage.
"""
ch_names = []
pos = []
file_format = op.splitext(fname)[1].lower()
with open(fname) as f:
if file_format != ".xyz":
f.readline() # skip header
delimiter = "," if file_format == ".csv" else "\t"
for row in csv.reader(f, delimiter=delimiter):
if file_format == ".xyz":
_, x, y, z, ch_name, *_ = row
ch_name = ch_name.strip() # deals with variable tab size
else:
ch_name, x, y, z, *_ = row
ch_names.append(ch_name)
pos.append((x, y, z))
d = _check_dupes_odict(ch_names, np.array(pos, dtype=float))
return make_dig_montage(ch_pos=d)
mne/channels/channels.py
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mne/channels/data/layouts/CTF-275.lout
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-42.27 42.33 -39.99 31.80
001 -4.09 10.91 4.00 3.00 MLC11-2622
002 -7.25 8.87 4.00 3.00 MLC12-2622
003 -10.79 7.43 4.00 3.00 MLC13-2622
004 -14.40 5.31 4.00 3.00 MLC14-2622
005 -17.45 2.88 4.00 3.00 MLC15-2622
006 -19.94 -0.21 4.00 3.00 MLC16-2622
007 -22.30 -3.88 4.00 3.00 MLC17-2622
008 -7.70 5.16 4.00 3.00 MLC21-2622
009 -11.18 3.69 4.00 3.00 MLC22-2622
010 -14.17 1.40 4.00 3.00 MLC23-2622
011 -16.42 -1.52 4.00 3.00 MLC24-2622
012 -18.64 -4.88 4.00 3.00 MLC25-2622
013 -12.55 -2.00 4.00 3.00 MLC31-2622
014 -15.13 -5.41 4.00 3.00 MLC32-2622
015 -9.57 0.28 4.00 3.00 MLC41-2622
016 -11.51 -5.56 4.00 3.00 MLC42-2622
017 -4.04 4.58 4.00 3.00 MLC51-2622
018 -6.04 1.35 4.00 3.00 MLC52-2622
019 -8.79 -3.34 4.00 3.00 MLC53-2622
020 -8.32 -7.10 4.00 3.00 MLC54-2622
021 -6.60 -10.22 4.00 3.00 MLC55-2622
022 -4.01 -1.76 4.00 3.00 MLC61-2622
023 -5.55 -4.97 4.00 3.00 MLC62-2622
024 -3.74 -8.12 4.00 3.00 MLC63-2622
025 -7.63 28.14 4.00 3.00 MLF11-2622
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mne/channels/data/layouts/EGI256.lout
+259
View File
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128 0.1989990214 0.07992534312 0.03188472676 0.02713339699 MEG 128
129 -0.2893372271 -0.3287872551 0.03188472676 0.02713339699 MEG 129
130 -0.2271848223 -0.3869397712 0.03188472676 0.02713339699 MEG 130
131 -0.1541857375 -0.4262216107 0.03188472676 0.02713339699 MEG 131
132 -0.07352758894 -0.45 0.03188472676 0.02713339699 MEG 132
133 -0.2537949872 -0.2821367359 0.03188472676 0.02713339699 MEG 133
134 -0.05789427224 -0.3854591968 0.03188472676 0.02713339699 MEG 134
135 -0.2180547699 -0.2381599549 0.03188472676 0.02713339699 MEG 135
136 -0.1654458556 -0.2836078687 0.03188472676 0.02713339699 MEG 136
137 -0.1282115583 -0.3636295479 0.03188472676 0.02713339699 MEG 137
138 -0.1869048087 -0.2008317859 0.03188472676 0.02713339699 MEG 138
139 -0.092414085 -0.2712662931 0.03188472676 0.02713339699 MEG 139
140 -0.04464737067 -0.3262626614 0.03188472676 0.02713339699 MEG 140
141 -0.1084802139 -0.2080301215 0.03188472676 0.02713339699 MEG 141
142 -0.008347885446 -0.2337992621 0.03188472676 0.02713339699 MEG 142
143 -0.1346335691 -0.1372039628 0.03188472676 0.02713339699 MEG 143
144 -0.05019800217 -0.1837431338 0.03188472676 0.02713339699 MEG 144
145 0.08934861049 -0.3726540907 0.03188472676 0.02713339699 MEG 145
146 0.106172944 -0.4435639299 0.03188472676 0.02713339699 MEG 146
147 0.2218612721 0.2682623426 0.03188472676 0.02713339699 MEG 147
148 0.2670027616 -0.3555067924 0.03188472676 0.02713339699 MEG 148
149 0.01625933145 -0.3877305948 0.03188472676 0.02713339699 MEG 149
150 0.1626843509 -0.342433819 0.03188472676 0.02713339699 MEG 150
151 0.07960240502 -0.3134489525 0.03188472676 0.02713339699 MEG 151
152 0.1456745581 -0.2843789795 0.03188472676 0.02713339699 MEG 152
153 0.2343957441 -0.2951725192 0.03188472676 0.02713339699 MEG 153
154 0.01783071962 -0.2738066839 0.03188472676 0.02713339699 MEG 154
155 0.1274451621 -0.2352949445 0.03188472676 0.02713339699 MEG 155
156 0.1746647823 -0.1970760538 0.03188472676 0.02713339699 MEG 156
157 0.0872449245 -0.2076405522 0.03188472676 0.02713339699 MEG 157
158 0.03888945369 -0.183846741 0.03188472676 0.02713339699 MEG 158
159 0.08527772847 -0.1590694194 0.03188472676 0.02713339699 MEG 159
160 0.1230026134 -0.1283845973 0.03188472676 0.02713339699 MEG 160
161 -0.547000000 -0.5000000000 0.03188472676 0.02713339699 COMNT
162 -0.547000000 -0.5000000000 0.03188472676 0.02713339699 SCALE
mne/channels/data/layouts/KIT-AD.lout
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001 0.61 0.56 0.02 0.04 MEG 001
002 0.59 0.50 0.02 0.04 MEG 002
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004 0.52 0.43 0.02 0.04 MEG 004
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007 0.52 0.70 0.02 0.04 MEG 007
008 0.58 0.59 0.02 0.04 MEG 008
009 0.47 0.71 0.02 0.04 MEG 009
010 0.53 0.49 0.02 0.04 MEG 010
011 0.57 0.53 0.02 0.04 MEG 011
012 0.43 0.50 0.02 0.04 MEG 012
013 0.40 0.55 0.02 0.04 MEG 013
014 0.57 0.39 0.02 0.04 MEG 014
015 0.38 0.41 0.02 0.04 MEG 015
016 0.48 0.37 0.02 0.04 MEG 016
017 0.16 0.84 0.02 0.04 MEG 017
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021 0.53 0.56 0.02 0.04 MEG 021
022 0.44 0.57 0.02 0.04 MEG 022
023 0.56 0.46 0.02 0.04 MEG 023
024 0.59 0.68 0.02 0.04 MEG 024
025 0.34 0.86 0.02 0.04 MEG 025
026 0.39 0.89 0.02 0.04 MEG 026
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028 0.61 0.87 0.02 0.04 MEG 028
029 0.66 0.84 0.02 0.04 MEG 029
030 0.59 0.76 0.02 0.04 MEG 030
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035 0.36 0.59 0.02 0.04 MEG 035
036 0.38 0.70 0.02 0.04 MEG 036
037 0.07 0.87 0.02 0.04 MEG 037
038 0.24 0.61 0.02 0.04 MEG 038
039 0.32 0.68 0.02 0.04 MEG 039
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051 0.32 0.76 0.02 0.04 MEG 051
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053 0.27 0.33 0.02 0.04 MEG 053
054 0.17 0.74 0.02 0.04 MEG 054
055 0.05 0.65 0.02 0.04 MEG 055
056 0.28 0.63 0.02 0.04 MEG 056
057 0.70 0.62 0.02 0.04 MEG 057
058 0.94 0.38 0.02 0.04 MEG 058
059 0.91 0.73 0.02 0.04 MEG 059
060 0.82 0.93 0.02 0.04 MEG 060
061 0.93 0.63 0.02 0.04 MEG 061
062 0.75 0.78 0.02 0.04 MEG 062
063 0.69 0.78 0.02 0.04 MEG 063
064 0.43 0.00 0.02 0.04 MEG 064
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066 0.19 0.29 0.02 0.04 MEG 066
067 0.15 0.56 0.02 0.04 MEG 067
068 0.33 0.53 0.02 0.04 MEG 068
069 0.35 0.47 0.02 0.04 MEG 069
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073 0.67 0.75 0.02 0.04 MEG 073
074 0.74 0.86 0.02 0.04 MEG 074
075 0.81 0.71 0.02 0.04 MEG 075
076 0.78 0.62 0.02 0.04 MEG 076
077 0.65 0.65 0.02 0.04 MEG 077
078 0.83 0.81 0.02 0.04 MEG 078
079 0.82 0.53 0.02 0.04 MEG 079
080 0.78 0.36 0.02 0.04 MEG 080
081 0.56 0.65 0.02 0.04 MEG 081
082 0.35 0.74 0.02 0.04 MEG 082
083 0.21 0.71 0.02 0.04 MEG 083
084 0.12 0.75 0.02 0.04 MEG 084
085 0.11 0.66 0.02 0.04 MEG 085
086 0.21 0.92 0.02 0.04 MEG 086
087 0.13 0.96 0.02 0.04 MEG 087
088 0.03 0.76 0.02 0.04 MEG 088
089 0.66 0.89 0.02 0.04 MEG 089
090 0.61 0.93 0.02 0.04 MEG 090
091 0.63 0.79 0.02 0.04 MEG 091
092 0.71 0.84 0.02 0.04 MEG 092
093 0.44 0.91 0.02 0.04 MEG 093
094 0.56 0.89 0.02 0.04 MEG 094
095 0.42 0.68 0.02 0.04 MEG 095
096 0.54 0.79 0.02 0.04 MEG 096
097 0.11 0.86 0.02 0.04 MEG 097
098 0.14 0.36 0.02 0.04 MEG 098
099 0.32 0.60 0.02 0.04 MEG 099
100 0.25 0.45 0.02 0.04 MEG 100
101 0.19 0.54 0.02 0.04 MEG 101
102 0.27 0.85 0.02 0.04 MEG 102
103 0.27 0.75 0.02 0.04 MEG 103
104 0.01 0.64 0.02 0.04 MEG 104
105 0.69 0.68 0.02 0.04 MEG 105
106 0.88 0.82 0.02 0.04 MEG 106
107 0.45 0.80 0.02 0.04 MEG 107
108 0.50 0.86 0.02 0.04 MEG 108
109 0.36 0.80 0.02 0.04 MEG 109
110 0.49 0.96 0.02 0.04 MEG 110
111 0.37 0.93 0.02 0.04 MEG 111
112 0.32 0.90 0.02 0.04 MEG 112
113 0.07 0.42 0.02 0.04 MEG 113
114 0.73 0.72 0.02 0.04 MEG 114
115 0.19 0.12 0.02 0.04 MEG 115
116 0.01 0.51 0.02 0.04 MEG 116
117 0.07 0.29 0.02 0.04 MEG 117
118 0.16 0.47 0.02 0.04 MEG 118
119 0.22 0.33 0.02 0.04 MEG 119
120 0.10 0.54 0.02 0.04 MEG 120
121 0.78 0.89 0.02 0.04 MEG 121
122 0.87 0.63 0.02 0.04 MEG 122
123 0.86 0.72 0.02 0.04 MEG 123
124 0.77 0.70 0.02 0.04 MEG 124
125 0.63 0.71 0.02 0.04 MEG 125
126 0.89 0.27 0.02 0.04 MEG 126
127 0.97 0.62 0.02 0.04 MEG 127
128 0.83 0.62 0.02 0.04 MEG 128
129 0.77 0.11 0.02 0.04 MEG 129
130 0.86 0.95 0.02 0.04 MEG 130
131 0.71 0.42 0.02 0.04 MEG 131
132 0.78 0.53 0.02 0.04 MEG 132
133 0.65 0.57 0.02 0.04 MEG 133
134 0.16 0.67 0.02 0.04 MEG 134
135 0.29 0.71 0.02 0.04 MEG 135
136 0.16 0.23 0.02 0.04 MEG 136
137 0.82 0.34 0.02 0.04 MEG 137
138 0.87 0.52 0.02 0.04 MEG 138
139 0.81 0.22 0.02 0.04 MEG 139
140 0.90 0.40 0.02 0.04 MEG 140
141 0.97 0.49 0.02 0.04 MEG 141
142 0.74 0.30 0.02 0.04 MEG 142
143 0.81 0.44 0.02 0.04 MEG 143
144 0.95 0.75 0.02 0.04 MEG 144
145 0.13 0.19 0.02 0.04 MEG 145
146 0.28 0.56 0.02 0.04 MEG 146
147 0.74 0.15 0.02 0.04 MEG 147
148 0.10 0.33 0.02 0.04 MEG 148
149 0.35 0.02 0.02 0.04 MEG 149
150 0.03 0.39 0.02 0.04 MEG 150
151 0.27 0.06 0.02 0.04 MEG 151
152 0.31 0.43 0.02 0.04 MEG 152
153 0.77 0.26 0.02 0.04 MEG 153
154 0.67 0.10 0.02 0.04 MEG 154
155 0.76 0.44 0.02 0.04 MEG 155
156 0.83 0.18 0.02 0.04 MEG 156
157 0.61 0.02 0.02 0.04 MEG 157
158 0.91 0.86 0.02 0.04 MEG 158
159 0.92 0.51 0.02 0.04 MEG 159
160 0.86 0.30 0.02 0.04 MEG 160
161 0.44 0.12 0.02 0.04 MEG 161
162 0.37 0.30 0.02 0.04 MEG 162
163 0.30 0.17 0.02 0.04 MEG 163
164 0.36 0.25 0.02 0.04 MEG 164
165 0.41 0.22 0.02 0.04 MEG 165
166 0.31 0.28 0.02 0.04 MEG 166
167 0.05 0.53 0.02 0.04 MEG 167
168 0.08 0.76 0.02 0.04 MEG 168
169 0.69 0.24 0.02 0.04 MEG 169
170 0.57 0.18 0.02 0.04 MEG 170
171 0.50 0.17 0.02 0.04 MEG 171
172 0.64 0.20 0.02 0.04 MEG 172
173 0.65 0.42 0.02 0.04 MEG 173
174 0.69 0.53 0.02 0.04 MEG 174
175 0.61 0.44 0.02 0.04 MEG 175
176 0.70 0.32 0.02 0.04 MEG 176
177 0.44 0.17 0.02 0.04 MEG 177
178 0.38 0.18 0.02 0.04 MEG 178
179 0.32 0.22 0.02 0.04 MEG 179
180 0.44 0.06 0.02 0.04 MEG 180
181 0.22 0.16 0.02 0.04 MEG 181
182 0.36 0.07 0.02 0.04 MEG 182
183 0.28 0.11 0.02 0.04 MEG 183
184 0.42 0.27 0.02 0.04 MEG 184
185 0.52 0.32 0.02 0.04 MEG 185
186 0.57 0.33 0.02 0.04 MEG 186
187 0.47 0.32 0.02 0.04 MEG 187
188 0.62 0.37 0.02 0.04 MEG 188
189 0.73 0.49 0.02 0.04 MEG 189
190 0.67 0.36 0.02 0.04 MEG 190
191 0.74 0.57 0.02 0.04 MEG 191
192 0.64 0.49 0.02 0.04 MEG 192
193 0.59 0.06 0.02 0.04 MEG 193
194 0.52 -0.00 0.02 0.04 MEG 194
195 0.58 0.29 0.02 0.04 MEG 195
196 0.53 0.27 0.02 0.04 MEG 196
197 0.47 0.26 0.02 0.04 MEG 197
198 0.34 0.39 0.02 0.04 MEG 198
199 0.42 0.33 0.02 0.04 MEG 199
200 0.38 0.35 0.02 0.04 MEG 200
201 0.53 0.22 0.02 0.04 MEG 201
202 0.59 0.24 0.02 0.04 MEG 202
203 0.65 0.27 0.02 0.04 MEG 203
204 0.27 0.26 0.02 0.04 MEG 204
205 0.51 0.11 0.02 0.04 MEG 205
206 0.65 0.15 0.02 0.04 MEG 206
207 0.51 0.05 0.02 0.04 MEG 207
208 0.69 0.05 0.02 0.04 MEG 208
mne/channels/data/layouts/KIT-AS-2008.lout
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114 0.98 0.42 0.10 0.05 MEG 115
115 1.00 0.54 0.10 0.05 MEG 116
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117 0.93 0.45 0.10 0.05 MEG 118
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132 0.47 0.06 0.10 0.05 MEG 133
133 0.25 0.21 0.10 0.05 MEG 134
134 0.32 0.17 0.10 0.05 MEG 135
135 0.39 0.13 0.10 0.05 MEG 136
136 0.29 0.26 0.10 0.05 MEG 137
137 0.41 0.19 0.10 0.05 MEG 138
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142 0.45 0.29 0.10 0.05 MEG 143
143 0.50 0.28 0.10 0.05 MEG 144
144 0.65 0.03 0.10 0.05 MEG 145
145 0.82 0.13 0.10 0.05 MEG 146
146 0.55 0.06 0.10 0.05 MEG 147
147 0.71 0.12 0.10 0.05 MEG 148
148 0.62 0.14 0.10 0.05 MEG 149
149 0.69 0.18 0.10 0.05 MEG 150
150 0.76 0.23 0.10 0.05 MEG 151
151 0.54 0.18 0.10 0.05 MEG 152
152 0.61 0.20 0.10 0.05 MEG 153
153 0.73 0.27 0.10 0.05 MEG 154
154 0.63 0.25 0.10 0.05 MEG 155
155 0.56 0.28 0.10 0.05 MEG 156
156 0.67 0.35 0.10 0.05 MEG 157
mne/channels/data/layouts/KIT-UMD-3.lout
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mne/channels/data/layouts/Neuromag_122.lout
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mne/channels/data/layouts/Vectorview-all.lout
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mne/channels/data/layouts/Vectorview-grad.lout
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64 0.496189 -1.527114 0.290000 0.230000 O2
mne/channels/data/layouts/magnesWH3600.lout
+249
View File
@@ -0,0 +1,249 @@
-42.19 43.52 -41.70 28.71
001 -1.28 -5.13 4.00 3.00 MEG 001
002 -1.22 -1.43 4.00 3.00 MEG 002
003 -1.37 2.53 4.00 3.00 MEG 003
004 -1.36 5.90 4.00 3.00 MEG 004
005 -1.45 9.27 4.00 3.00 MEG 005
006 -4.89 9.36 4.00 3.00 MEG 006
007 -5.20 5.86 4.00 3.00 MEG 007
008 -5.26 2.40 4.00 3.00 MEG 008
009 -5.34 -1.29 4.00 3.00 MEG 009
010 -5.12 -5.08 4.00 3.00 MEG 010
011 -4.73 -8.47 4.00 3.00 MEG 011
012 -1.31 -8.81 4.00 3.00 MEG 012
013 2.04 -8.49 4.00 3.00 MEG 013
014 2.54 -5.16 4.00 3.00 MEG 014
015 2.69 -1.43 4.00 3.00 MEG 015
016 2.62 2.56 4.00 3.00 MEG 016
017 2.50 5.89 4.00 3.00 MEG 017
018 2.10 9.34 4.00 3.00 MEG 018
019 -1.45 12.55 4.00 3.00 MEG 019
020 -5.76 12.42 4.00 3.00 MEG 020
021 -8.30 9.98 4.00 3.00 MEG 021
022 -9.16 5.97 4.00 3.00 MEG 022
023 -9.32 2.49 4.00 3.00 MEG 023
024 -9.42 -1.32 4.00 3.00 MEG 024
025 -9.13 -5.11 4.00 3.00 MEG 025
026 -8.43 -9.18 4.00 3.00 MEG 026
027 -5.45 -12.10 4.00 3.00 MEG 027
028 -1.40 -12.51 4.00 3.00 MEG 028
029 2.64 -12.08 4.00 3.00 MEG 029
030 5.77 -9.29 4.00 3.00 MEG 030
031 6.50 -5.19 4.00 3.00 MEG 031
032 6.85 -1.37 4.00 3.00 MEG 032
033 6.70 2.65 4.00 3.00 MEG 033
034 6.46 6.18 4.00 3.00 MEG 034
035 5.61 10.08 4.00 3.00 MEG 035
036 2.95 12.49 4.00 3.00 MEG 036
037 -1.47 15.77 4.00 3.00 MEG 037
038 -5.48 15.52 4.00 3.00 MEG 038
039 -8.97 13.31 4.00 3.00 MEG 039
040 -11.91 10.42 4.00 3.00 MEG 040
041 -12.96 6.84 4.00 3.00 MEG 041
042 -13.39 3.21 4.00 3.00 MEG 042
043 -13.58 -0.70 4.00 3.00 MEG 043
044 -13.08 -4.42 4.00 3.00 MEG 044
045 -12.52 -8.05 4.00 3.00 MEG 045
046 -11.13 -11.34 4.00 3.00 MEG 046
047 -8.45 -14.21 4.00 3.00 MEG 047
048 -5.08 -15.56 4.00 3.00 MEG 048
049 -1.60 -16.17 4.00 3.00 MEG 049
050 2.22 -15.61 4.00 3.00 MEG 050
051 5.63 -14.28 4.00 3.00 MEG 051
052 8.38 -11.70 4.00 3.00 MEG 052
053 9.89 -8.24 4.00 3.00 MEG 053
054 10.43 -4.42 4.00 3.00 MEG 054
055 10.94 -0.62 4.00 3.00 MEG 055
056 10.72 3.35 4.00 3.00 MEG 056
057 10.22 7.01 4.00 3.00 MEG 057
058 9.04 10.61 4.00 3.00 MEG 058
059 6.20 13.42 4.00 3.00 MEG 059
060 2.52 15.65 4.00 3.00 MEG 060
061 -1.53 18.91 4.00 3.00 MEG 061
062 -5.68 18.61 4.00 3.00 MEG 062
063 -9.46 16.89 4.00 3.00 MEG 063
064 -12.95 14.48 4.00 3.00 MEG 064
065 -15.67 11.24 4.00 3.00 MEG 065
066 -17.06 7.05 4.00 3.00 MEG 066
067 -17.65 3.16 4.00 3.00 MEG 067
068 -17.98 -1.20 4.00 3.00 MEG 068
069 -17.13 -5.53 4.00 3.00 MEG 069
070 -16.60 -9.33 4.00 3.00 MEG 070
071 -14.32 -12.91 4.00 3.00 MEG 071
072 -11.85 -15.75 4.00 3.00 MEG 072
073 -8.78 -17.93 4.00 3.00 MEG 073
074 -5.30 -19.40 4.00 3.00 MEG 074
075 -1.58 -19.85 4.00 3.00 MEG 075
076 2.41 -19.42 4.00 3.00 MEG 076
077 5.94 -18.13 4.00 3.00 MEG 077
078 9.16 -15.98 4.00 3.00 MEG 078
079 11.79 -13.08 4.00 3.00 MEG 079
080 13.62 -9.59 4.00 3.00 MEG 080
081 14.57 -5.64 4.00 3.00 MEG 081
082 15.42 -1.35 4.00 3.00 MEG 082
083 15.05 3.30 4.00 3.00 MEG 083
084 14.29 7.20 4.00 3.00 MEG 084
085 12.81 11.43 4.00 3.00 MEG 085
086 9.96 14.67 4.00 3.00 MEG 086
087 6.46 17.06 4.00 3.00 MEG 087
088 2.60 18.73 4.00 3.00 MEG 088
089 -1.60 22.21 4.00 3.00 MEG 089
090 -5.83 21.82 4.00 3.00 MEG 090
091 -9.75 20.43 4.00 3.00 MEG 091
092 -13.45 18.45 4.00 3.00 MEG 092
093 -16.67 15.62 4.00 3.00 MEG 093
094 -19.33 12.13 4.00 3.00 MEG 094
095 -20.94 7.82 4.00 3.00 MEG 095
096 -21.81 3.65 4.00 3.00 MEG 096
097 -22.23 -1.27 4.00 3.00 MEG 097
098 -21.14 -5.87 4.00 3.00 MEG 098
099 -20.30 -9.97 4.00 3.00 MEG 099
100 -18.46 -13.84 4.00 3.00 MEG 100
101 -16.07 -17.08 4.00 3.00 MEG 101
102 -12.88 -19.71 4.00 3.00 MEG 102
103 -9.34 -21.89 4.00 3.00 MEG 103
104 -5.64 -23.02 4.00 3.00 MEG 104
105 -1.72 -23.54 4.00 3.00 MEG 105
106 2.48 -23.24 4.00 3.00 MEG 106
107 6.42 -22.00 4.00 3.00 MEG 107
108 9.86 -20.19 4.00 3.00 MEG 108
109 13.22 -17.32 4.00 3.00 MEG 109
110 15.75 -14.15 4.00 3.00 MEG 110
111 17.67 -10.19 4.00 3.00 MEG 111
112 18.65 -6.08 4.00 3.00 MEG 112
113 19.69 -1.27 4.00 3.00 MEG 113
114 19.27 3.70 4.00 3.00 MEG 114
115 18.30 8.05 4.00 3.00 MEG 115
116 16.46 12.48 4.00 3.00 MEG 116
117 13.74 15.93 4.00 3.00 MEG 117
118 10.41 18.72 4.00 3.00 MEG 118
119 6.64 20.69 4.00 3.00 MEG 119
120 2.67 22.02 4.00 3.00 MEG 120
121 -1.74 25.41 4.00 3.00 MEG 121
122 -6.59 24.84 4.00 3.00 MEG 122
123 -11.16 23.37 4.00 3.00 MEG 123
124 -15.46 21.07 4.00 3.00 MEG 124
125 -19.25 17.84 4.00 3.00 MEG 125
126 -22.45 13.89 4.00 3.00 MEG 126
127 -24.89 8.96 4.00 3.00 MEG 127
128 -26.13 4.36 4.00 3.00 MEG 128
129 -26.65 -1.22 4.00 3.00 MEG 129
130 -25.30 -6.36 4.00 3.00 MEG 130
131 -24.16 -11.45 4.00 3.00 MEG 131
132 -21.98 -15.88 4.00 3.00 MEG 132
133 -18.81 -19.82 4.00 3.00 MEG 133
134 -15.20 -22.99 4.00 3.00 MEG 134
135 -11.11 -25.29 4.00 3.00 MEG 135
136 -6.51 -26.74 4.00 3.00 MEG 136
137 -1.86 -27.28 4.00 3.00 MEG 137
138 3.17 -26.90 4.00 3.00 MEG 138
139 7.79 -25.55 4.00 3.00 MEG 139
140 12.07 -23.15 4.00 3.00 MEG 140
141 15.93 -20.09 4.00 3.00 MEG 141
142 19.04 -16.25 4.00 3.00 MEG 142
143 21.39 -11.67 4.00 3.00 MEG 143
144 22.75 -6.58 4.00 3.00 MEG 144
145 23.99 -1.23 4.00 3.00 MEG 145
146 23.36 4.49 4.00 3.00 MEG 146
147 22.02 9.37 4.00 3.00 MEG 147
148 19.51 14.31 4.00 3.00 MEG 148
149 16.20 18.23 4.00 3.00 MEG 149
150 12.16 21.54 4.00 3.00 MEG 150
151 7.85 23.69 4.00 3.00 MEG 151
152 3.16 25.01 4.00 3.00 MEG 152
153 -23.01 18.82 4.00 3.00 MEG 153
154 -26.06 15.31 4.00 3.00 MEG 154
155 -28.76 10.18 4.00 3.00 MEG 155
156 -31.71 3.39 4.00 3.00 MEG 156
157 -32.05 -2.89 4.00 3.00 MEG 157
158 -31.42 -8.67 4.00 3.00 MEG 158
159 -26.22 -15.24 4.00 3.00 MEG 159
160 -23.31 -19.72 4.00 3.00 MEG 160
161 -19.33 -23.66 4.00 3.00 MEG 161
162 -14.75 -26.73 4.00 3.00 MEG 162
163 -9.92 -28.91 4.00 3.00 MEG 163
164 -4.52 -30.10 4.00 3.00 MEG 164
165 1.25 -30.15 4.00 3.00 MEG 165
166 6.17 -29.40 4.00 3.00 MEG 166
167 11.43 -27.39 4.00 3.00 MEG 167
168 16.20 -24.37 4.00 3.00 MEG 168
169 20.37 -20.27 4.00 3.00 MEG 169
170 23.54 -15.56 4.00 3.00 MEG 170
171 28.66 -8.94 4.00 3.00 MEG 171
172 29.46 -3.00 4.00 3.00 MEG 172
173 29.04 3.51 4.00 3.00 MEG 173
174 25.94 10.77 4.00 3.00 MEG 174
175 23.08 15.80 4.00 3.00 MEG 175
176 19.78 19.54 4.00 3.00 MEG 176
177 -26.70 20.52 4.00 3.00 MEG 177
178 -29.66 16.81 4.00 3.00 MEG 178
179 -32.55 11.68 4.00 3.00 MEG 179
180 -32.47 -13.23 4.00 3.00 MEG 180
181 -27.63 -19.12 4.00 3.00 MEG 181
182 -23.75 -23.89 4.00 3.00 MEG 182
183 -18.94 -27.77 4.00 3.00 MEG 183
184 -13.64 -30.59 4.00 3.00 MEG 184
185 -7.93 -32.70 4.00 3.00 MEG 185
186 -2.12 -33.31 4.00 3.00 MEG 186
187 4.06 -32.74 4.00 3.00 MEG 187
188 10.04 -31.14 4.00 3.00 MEG 188
189 15.57 -28.41 4.00 3.00 MEG 189
190 20.44 -24.69 4.00 3.00 MEG 190
191 24.62 -19.81 4.00 3.00 MEG 191
192 29.49 -13.87 4.00 3.00 MEG 192
193 29.48 12.54 4.00 3.00 MEG 193
194 26.49 17.54 4.00 3.00 MEG 194
195 23.28 21.40 4.00 3.00 MEG 195
196 -36.84 4.15 4.00 3.00 MEG 196
197 -37.22 -3.16 4.00 3.00 MEG 197
198 -36.14 -9.68 4.00 3.00 MEG 198
199 -28.42 -23.63 4.00 3.00 MEG 199
200 -23.68 -28.05 4.00 3.00 MEG 200
201 -18.03 -31.89 4.00 3.00 MEG 201
202 -11.97 -34.42 4.00 3.00 MEG 202
203 -5.32 -35.88 4.00 3.00 MEG 203
204 1.03 -36.08 4.00 3.00 MEG 204
205 7.92 -35.00 4.00 3.00 MEG 205
206 13.99 -32.64 4.00 3.00 MEG 206
207 19.78 -29.06 4.00 3.00 MEG 207
208 24.79 -24.52 4.00 3.00 MEG 208
209 33.39 -10.13 4.00 3.00 MEG 209
210 34.62 -3.11 4.00 3.00 MEG 210
211 34.23 4.57 4.00 3.00 MEG 211
212 -32.38 19.14 4.00 3.00 MEG 212
213 -35.90 13.21 4.00 3.00 MEG 213
214 -36.70 -14.70 4.00 3.00 MEG 214
215 -32.93 -22.44 4.00 3.00 MEG 215
216 -28.17 -28.07 4.00 3.00 MEG 216
217 -22.65 -32.41 4.00 3.00 MEG 217
218 -16.53 -35.71 4.00 3.00 MEG 218
219 -9.52 -37.92 4.00 3.00 MEG 219
220 -2.58 -38.82 4.00 3.00 MEG 220
221 4.65 -38.54 4.00 3.00 MEG 221
222 11.78 -36.65 4.00 3.00 MEG 222
223 18.43 -33.60 4.00 3.00 MEG 223
224 24.26 -29.21 4.00 3.00 MEG 224
225 29.52 -23.44 4.00 3.00 MEG 225
226 33.73 -15.36 4.00 3.00 MEG 226
227 33.02 14.20 4.00 3.00 MEG 227
228 29.24 19.93 4.00 3.00 MEG 228
229 -36.80 18.24 4.00 3.00 MEG 229
230 -40.03 12.76 4.00 3.00 MEG 230
231 -41.35 5.03 4.00 3.00 MEG 231
232 -41.79 -3.17 4.00 3.00 MEG 232
233 -40.48 -10.59 4.00 3.00 MEG 233
234 -32.92 -26.79 4.00 3.00 MEG 234
235 -27.40 -32.12 4.00 3.00 MEG 235
236 -20.92 -36.72 4.00 3.00 MEG 236
237 -14.11 -39.49 4.00 3.00 MEG 237
238 -6.76 -41.18 4.00 3.00 MEG 238
239 1.45 -41.40 4.00 3.00 MEG 239
240 8.96 -40.25 4.00 3.00 MEG 240
241 16.27 -37.84 4.00 3.00 MEG 241
242 22.75 -33.68 4.00 3.00 MEG 242
243 29.08 -28.20 4.00 3.00 MEG 243
244 37.59 -11.05 4.00 3.00 MEG 244
245 39.12 -3.16 4.00 3.00 MEG 245
246 38.59 5.47 4.00 3.00 MEG 246
247 37.16 13.60 4.00 3.00 MEG 247
248 33.62 18.93 4.00 3.00 MEG 248
mne/channels/data/montages/EGI_256.csd
+258
View File
@@ -0,0 +1,258 @@
// MatLab Sphere coordinates [degrees] Cartesian coordinates
// Label Theta Phi Radius X Y Z off sphere surface
E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011
E2 44.600 -0.880 1.000 0.7119 0.7021 -0.0154 0.00000000000000000
E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000
E4 58.200 21.800 1.000 0.4893 0.7891 0.3714 -0.00000000000000011
E5 64.200 33.600 1.000 0.3625 0.7499 0.5534 0.00000000000000000
E6 70.800 45.500 1.000 0.2305 0.6619 0.7133 -0.00000000000000022
E7 77.200 56.700 1.000 0.1216 0.5354 0.8358 0.00000000000000000
E8 90.000 67.700 1.000 0.0000 0.3795 0.9252 0.00000000000000000
E9 127.300 78.300 1.000 -0.1229 0.1613 0.9792 0.00000000000000000
E10 51.200 -9.080 1.000 0.6188 0.7696 -0.1578 0.00000000000000000
E11 58.800 2.370 1.000 0.5176 0.8546 0.0414 0.00000000000000000
E12 66.800 14.300 1.000 0.3817 0.8907 0.2470 -0.00000000000000011
E13 73.800 25.200 1.000 0.2524 0.8689 0.4258 0.00000000000000022
E14 81.360 36.400 1.000 0.1209 0.7958 0.5934 0.00000000000000022
E15 90.000 46.900 1.000 0.0000 0.6833 0.7302 0.00000000000000000
E16 102.800 56.700 1.000 -0.1216 0.5354 0.8358 0.00000000000000000
E17 128.200 66.600 1.000 -0.2456 0.3121 0.9178 -0.00000000000000011
E18 66.600 -4.970 1.000 0.3957 0.9143 -0.0866 -0.00000000000000011
E19 74.000 4.680 1.000 0.2747 0.9581 0.0816 -0.00000000000000011
E20 81.960 15.700 1.000 0.1346 0.9532 0.2706 0.00000000000000022
E21 90.000 26.400 1.000 0.0000 0.8957 0.4446 0.00000000000000000
E22 98.640 36.400 1.000 -0.1209 0.7958 0.5934 0.00000000000000022
E23 109.200 45.500 1.000 -0.2305 0.6619 0.7133 0.00000000000000000
E24 127.200 54.200 1.000 -0.3537 0.4659 0.8111 0.00000000000000000
E25 82.540 -3.260 1.000 0.1296 0.9899 -0.0569 0.00000000000000000
E26 90.000 5.370 1.000 0.0000 0.9956 0.0936 0.00000000000000000
E27 98.040 15.700 1.000 -0.1346 0.9532 0.2706 0.00000000000000022
E28 106.200 25.200 1.000 -0.2524 0.8689 0.4258 0.00000000000000022
E29 115.800 33.600 1.000 -0.3625 0.7499 0.5534 0.00000000000000000
E30 128.800 41.200 1.000 -0.4715 0.5864 0.6587 0.00000000000000000
E31 90.000 -11.000 1.000 0.0000 0.9816 -0.1908 0.00000000000000000
E32 97.460 -3.260 1.000 -0.1296 0.9899 -0.0569 0.00000000000000000
E33 106.000 4.680 1.000 -0.2747 0.9581 0.0816 -0.00000000000000011
E34 113.200 14.300 1.000 -0.3817 0.8907 0.2470 -0.00000000000000022
E35 121.800 21.800 1.000 -0.4893 0.7891 0.3714 -0.00000000000000011
E36 128.500 30.200 1.000 -0.5380 0.6764 0.5030 0.00000000000000022
E37 113.400 -4.970 1.000 -0.3957 0.9143 -0.0866 0.00000000000000000
E38 121.200 2.370 1.000 -0.5176 0.8546 0.0414 0.00000000000000000
E39 128.300 11.000 1.000 -0.6084 0.7704 0.1908 0.00000000000000000
E40 135.300 20.800 1.000 -0.6645 0.6576 0.3551 0.00000000000000000
E41 140.600 32.000 1.000 -0.6553 0.5383 0.5299 0.00000000000000000
E42 144.500 44.000 1.000 -0.5856 0.4177 0.6947 0.00000000000000000
E43 151.000 54.800 1.000 -0.5042 0.2795 0.8171 0.00000000000000000
E44 163.200 66.400 1.000 -0.3833 0.1157 0.9164 0.00000000000000000
E45 197.000 77.300 1.000 -0.2102 -0.0643 0.9755 0.00000000000000000
E46 128.800 -9.080 1.000 -0.6188 0.7696 -0.1578 0.00000000000000000
E47 135.400 -0.880 1.000 -0.7119 0.7021 -0.0154 -0.00000000000000011
E48 142.500 8.460 1.000 -0.7847 0.6021 0.1471 0.00000000000000000
E49 149.200 19.400 1.000 -0.8102 0.4830 0.3322 0.00000000000000000
E50 155.300 32.200 1.000 -0.7688 0.3536 0.5329 0.00000000000000000
E51 162.400 44.200 1.000 -0.6834 0.2168 0.6972 0.00000000000000000
E52 173.500 54.500 1.000 -0.5770 0.0657 0.8141 0.00000000000000000
E53 197.000 65.600 1.000 -0.3951 -0.1208 0.9107 0.00000000000000000
E54 142.300 -14.000 1.000 -0.7677 0.5934 -0.2419 0.00000000000000000
E55 149.100 -4.100 1.000 -0.8559 0.5122 -0.0715 0.00000000000000000
E56 156.700 7.130 1.000 -0.9113 0.3925 0.1241 0.00000000000000022
E57 163.200 19.500 1.000 -0.9024 0.2725 0.3338 0.00000000000000000
E58 169.700 31.600 1.000 -0.8380 0.1523 0.5240 0.00000000000000000
E59 179.500 43.000 1.000 -0.7313 0.0064 0.6820 0.00000000000000000
E60 197.000 53.000 1.000 -0.5755 -0.1760 0.7986 0.00000000000000000
E61 158.000 -17.200 1.000 -0.8857 0.3579 -0.2957 -0.00000000000000022
E62 165.100 -5.730 1.000 -0.9615 0.2558 -0.0998 0.00000000000000022
E63 171.400 6.890 1.000 -0.9816 0.1485 0.1200 0.00000000000000022
E64 177.200 19.000 1.000 -0.9444 0.0462 0.3256 0.00000000000000000
E65 184.300 31.100 1.000 -0.8539 -0.0642 0.5165 0.00000000000000000
E66 196.000 39.900 1.000 -0.7374 -0.2115 0.6414 0.00000000000000000
E67 167.300 -27.900 1.000 -0.8621 0.1943 -0.4679 0.00000000000000000
E68 172.300 -17.500 1.000 -0.9451 0.1278 -0.3007 0.00000000000000000
E69 179.500 -6.970 1.000 -0.9926 0.0087 -0.1213 0.00000000000000000
E70 185.400 5.990 1.000 -0.9901 -0.0936 0.1044 0.00000000000000022
E71 191.000 18.700 1.000 -0.9298 -0.1807 0.3206 0.00000000000000000
E72 197.000 28.500 1.000 -0.8404 -0.2569 0.4772 0.00000000000000000
E73 174.500 -38.200 1.000 -0.7822 0.0753 -0.6184 0.00000000000000022
E74 193.000 -6.630 1.000 -0.9679 -0.2234 -0.1155 0.00000000000000000
E75 199.000 7.590 1.000 -0.9372 -0.3227 0.1321 0.00000000000000000
E76 205.000 19.800 1.000 -0.8527 -0.3976 0.3387 -0.00000000000000011
E77 209.000 31.900 1.000 -0.7425 -0.4116 0.5284 0.00000000000000000
E78 214.000 43.600 1.000 -0.6004 -0.4050 0.6896 0.00000000000000000
E79 221.000 55.600 1.000 -0.4264 -0.3707 0.8251 -0.00000000000000011
E80 233.000 67.400 1.000 -0.2313 -0.3069 0.9232 0.00000000000000000
E81 -90.000 78.400 1.000 0.0000 -0.2011 0.9796 -0.00000000000000011
E82 183.900 -45.800 1.000 -0.6956 -0.0474 -0.7169 0.00000000000000000
E83 205.000 -15.000 1.000 -0.8754 -0.4082 -0.2588 0.00000000000000000
E84 206.000 -3.510 1.000 -0.8971 -0.4375 -0.0612 -0.00000000000000022
E85 213.000 10.000 1.000 -0.8259 -0.5364 0.1736 -0.00000000000000011
E86 218.000 22.700 1.000 -0.7270 -0.5680 0.3859 0.00000000000000000
E87 225.000 35.300 1.000 -0.5771 -0.5771 0.5779 0.00000000000000000
E88 232.000 46.800 1.000 -0.4214 -0.5394 0.7290 -0.00000000000000011
E89 245.000 56.900 1.000 -0.2308 -0.4949 0.8377 -0.00000000000000011
E90 -90.000 67.500 1.000 0.0000 -0.3827 0.9239 0.00000000000000000
E91 195.000 -50.900 1.000 -0.6092 -0.1632 -0.7760 0.00000000000000000
E92 203.000 -42.700 1.000 -0.6765 -0.2872 -0.6782 -0.00000000000000022
E93 211.000 -32.500 1.000 -0.7229 -0.4344 -0.5373 0.00000000000000000
E94 212.000 -23.100 1.000 -0.7801 -0.4874 -0.3923 0.00000000000000000
E95 216.000 -12.400 1.000 -0.7901 -0.5741 -0.2147 0.00000000000000000
E96 221.000 0.666 1.000 -0.7547 -0.6560 0.0116 0.00000000000000000
E97 228.000 12.900 1.000 -0.6522 -0.7244 0.2233 0.00000000000000022
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mne/channels/data/montages/GSN-HydroCel-32.sfp
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mne/channels/data/montages/GSN-HydroCel-64_1.0.sfp
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mne/channels/data/montages/GSN-HydroCel-65_1.0.sfp
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mne/channels/data/montages/artinis-brite23.elc
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mne/channels/data/montages/artinis-octamon.elc
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# ASA optode file
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mne/channels/data/montages/biosemi128.txt
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Site Theta Phi
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mne/channels/data/montages/biosemi16.txt
+20
View File
@@ -0,0 +1,20 @@
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mne/channels/data/montages/biosemi160.txt
+164
View File
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mne/channels/data/montages/biosemi256.txt
+260
View File
@@ -0,0 +1,260 @@
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mne/channels/data/montages/biosemi32.txt
+36
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@@ -0,0 +1,36 @@
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mne/channels/data/montages/biosemi64.txt
+68
View File
@@ -0,0 +1,68 @@
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mne/channels/data/montages/brainproducts-RNP-BA-128.txt
+131
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@@ -0,0 +1,131 @@
Name Theta Phi
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mne/channels/data/montages/easycap-M1.txt
+75
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@@ -0,0 +1,75 @@
Site Theta Phi
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mne/channels/data/montages/easycap-M10.txt
+62
View File
@@ -0,0 +1,62 @@
Site Theta Phi
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mne/channels/data/montages/easycap-M43.txt
+65
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@@ -0,0 +1,65 @@
Site Theta Phi
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mne/channels/data/montages/mgh60.elc
+132
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@@ -0,0 +1,132 @@
# ASA electrode file
ReferenceLabel avg
UnitPosition mm
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mne/channels/data/montages/mgh70.elc
+152
View File
@@ -0,0 +1,152 @@
# ASA electrode file
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mne/channels/data/montages/standard_1005.elc
+698
View File
@@ -0,0 +1,698 @@
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Labels
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mne/channels/data/montages/standard_1020.elc
+200
View File
@@ -0,0 +1,200 @@
# ASA electrode file
ReferenceLabel avg
UnitPosition mm
NumberPositions= 97
Positions
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85.7939 -20.0093 -48.0310
0.0083 86.8110 -39.9830
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Labels
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Oz
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Iz
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T6
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mne/channels/data/montages/standard_alphabetic.elc
+142
View File
@@ -0,0 +1,142 @@
# ASA electrode file
ReferenceLabel avg
UnitPosition mm
NumberPositions= 68
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Labels
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mne/channels/data/montages/standard_postfixed.elc
+212
View File
@@ -0,0 +1,212 @@
# ASA electrode file
ReferenceLabel avg
UnitPosition mm
NumberPositions= 103
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Labels
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F3
F1
Fz
F2
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F8
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F5p
F3p
F1p
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F2p
F4p
F6p
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T1
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C5a
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C2a
C4a
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T3
C5
C3
C1
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C6
T4
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C6p
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P4a
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T6a
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P5
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Pz
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Cb2a
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P5p
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Oz
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Iz
Cb2p
M1
M2
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A2
mne/channels/data/montages/standard_prefixed.elc
+160
View File
@@ -0,0 +1,160 @@
# ASA electrode file
ReferenceLabel avg
UnitPosition mm
NumberPositions= 77
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85.7939 -45.0093 -68.0310
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Labels
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aF3
aF1
aFz
aF2
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F1
Fz
F2
F4
F6
F8
iT1
T1
pF5
pF3
pF1
pFz
pF2
pF4
pF6
T2
iT2
iT3
T3
C5
C3
C1
Cz
C2
C4
C6
T4
iT4
T3A
pC5
pC3
pC1
pCz
pC2
pC4
pC6
T4A
iT5
T5
P5
P3
P1
Pz
P2
P4
P6
T6
iT6
pO5
pO3
pO1
pOz
pO2
pO4
pO6
O1
Oz
O2
Iz
M1
M2
A1
A2
mne/channels/data/montages/standard_primed.elc
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# ASA electrode file
ReferenceLabel avg
UnitPosition mm
NumberPositions= 103
Positions
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85.7939 -25.0093 -68.0310
Labels
LPA
RPA
Nz
Fp1
Fpz
Fp2
F7'
F5'
F3'
F1'
Fz'
F2'
F4'
F6'
F8'
F7
F5
F3
F1
Fz
F2
F4
F6
F8
F7''
F5''
F3''
F1''
Fz''
F2''
F4''
F6''
F8''
T1
T3'
C5'
C3'
C1'
Cz'
C2'
C4'
C6'
T4'
T2
T3
C5
C3
C1
Cz
C2
C4
C6
T4
T3''
C5''
C3''
C1''
Cz''
C2''
C4''
C6''
T4''
T5'
P5'
P3'
P1'
Pz'
P2'
P4'
P6'
T6'
Cb1'
T5
P5
P3
P1
Pz
P2
P4
P6
T6
Cb2'
Cb1
O1'
P5''
P3''
P1''
Pz''
P2''
P4''
P6''
O2'
Cb2
O1
Oz
O2
Cb1''
Iz
Cb2''
M1
M2
A1
A2
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mne/channels/data/neighbors/__init__.py
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""Neighbor definitions for clustering permutation analysis."""
# This is a selection of files from http://fieldtrip.fcdonders.nl/template
# Additional definitions can be obtained through the FieldTrip software.
# For additional information on how these definitions were computed, please
# consider the related fieldtrip documentation:
# http://fieldtrip.fcdonders.nl/template/neighbours.
#
# KIT neighbor files were computed with ft_prepare_neighbours using the
# triangulation method.
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mne/channels/interpolation.py
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
from numpy.polynomial.legendre import legval
from scipy.interpolate import RectBivariateSpline
from scipy.linalg import pinv
from scipy.spatial.distance import pdist, squareform
from .._fiff.meas_info import _simplify_info
from .._fiff.pick import pick_channels, pick_info, pick_types
from ..surface import _normalize_vectors
from ..utils import _validate_type, logger, verbose, warn
def _calc_h(cosang, stiffness=4, n_legendre_terms=50):
"""Calculate spherical spline h function between points on a sphere.
Parameters
----------
cosang : array-like | float
cosine of angles between pairs of points on a spherical surface. This
is equivalent to the dot product of unit vectors.
stiffness : float
stiffnes of the spline. Also referred to as ``m``.
n_legendre_terms : int
number of Legendre terms to evaluate.
"""
factors = [
(2 * n + 1) / (n ** (stiffness - 1) * (n + 1) ** (stiffness - 1) * 4 * np.pi)
for n in range(1, n_legendre_terms + 1)
]
return legval(cosang, [0] + factors)
def _calc_g(cosang, stiffness=4, n_legendre_terms=50):
"""Calculate spherical spline g function between points on a sphere.
Parameters
----------
cosang : array-like of float, shape(n_channels, n_channels)
cosine of angles between pairs of points on a spherical surface. This
is equivalent to the dot product of unit vectors.
stiffness : float
stiffness of the spline.
n_legendre_terms : int
number of Legendre terms to evaluate.
Returns
-------
G : np.ndrarray of float, shape(n_channels, n_channels)
The G matrix.
"""
factors = [
(2 * n + 1) / (n**stiffness * (n + 1) ** stiffness * 4 * np.pi)
for n in range(1, n_legendre_terms + 1)
]
return legval(cosang, [0] + factors)
def _make_interpolation_matrix(pos_from, pos_to, alpha=1e-5):
"""Compute interpolation matrix based on spherical splines.
Implementation based on [1]
Parameters
----------
pos_from : np.ndarray of float, shape(n_good_sensors, 3)
The positions to interpolate from.
pos_to : np.ndarray of float, shape(n_bad_sensors, 3)
The positions to interpolate.
alpha : float
Regularization parameter. Defaults to 1e-5.
Returns
-------
interpolation : np.ndarray of float, shape(len(pos_from), len(pos_to))
The interpolation matrix that maps good signals to the location
of bad signals.
References
----------
[1] Perrin, F., Pernier, J., Bertrand, O. and Echallier, JF. (1989).
Spherical splines for scalp potential and current density mapping.
Electroencephalography Clinical Neurophysiology, Feb; 72(2):184-7.
"""
pos_from = pos_from.copy()
pos_to = pos_to.copy()
n_from = pos_from.shape[0]
n_to = pos_to.shape[0]
# normalize sensor positions to sphere
_normalize_vectors(pos_from)
_normalize_vectors(pos_to)
# cosine angles between source positions
cosang_from = pos_from.dot(pos_from.T)
cosang_to_from = pos_to.dot(pos_from.T)
G_from = _calc_g(cosang_from)
G_to_from = _calc_g(cosang_to_from)
assert G_from.shape == (n_from, n_from)
assert G_to_from.shape == (n_to, n_from)
if alpha is not None:
G_from.flat[:: len(G_from) + 1] += alpha
C = np.vstack(
[
np.hstack([G_from, np.ones((n_from, 1))]),
np.hstack([np.ones((1, n_from)), [[0]]]),
]
)
C_inv = pinv(C)
interpolation = np.hstack([G_to_from, np.ones((n_to, 1))]) @ C_inv[:, :-1]
assert interpolation.shape == (n_to, n_from)
return interpolation
def _do_interp_dots(inst, interpolation, goods_idx, bads_idx):
"""Dot product of channel mapping matrix to channel data."""
from ..epochs import BaseEpochs
from ..evoked import Evoked
from ..io import BaseRaw
_validate_type(inst, (BaseRaw, BaseEpochs, Evoked), "inst")
inst._data[..., bads_idx, :] = np.matmul(
interpolation, inst._data[..., goods_idx, :]
)
@verbose
def _interpolate_bads_eeg(inst, origin, exclude=None, ecog=False, verbose=None):
if exclude is None:
exclude = list()
bads_idx = np.zeros(len(inst.ch_names), dtype=bool)
goods_idx = np.zeros(len(inst.ch_names), dtype=bool)
picks = pick_types(inst.info, meg=False, eeg=not ecog, ecog=ecog, exclude=exclude)
inst.info._check_consistency()
bads_idx[picks] = [inst.ch_names[ch] in inst.info["bads"] for ch in picks]
if len(picks) == 0 or bads_idx.sum() == 0:
return
goods_idx[picks] = True
goods_idx[bads_idx] = False
pos = inst._get_channel_positions(picks)
# Make sure only EEG are used
bads_idx_pos = bads_idx[picks]
goods_idx_pos = goods_idx[picks]
# test spherical fit
distance = np.linalg.norm(pos - origin, axis=-1)
distance = np.mean(distance / np.mean(distance))
if np.abs(1.0 - distance) > 0.1:
warn(
"Your spherical fit is poor, interpolation results are "
"likely to be inaccurate."
)
pos_good = pos[goods_idx_pos] - origin
pos_bad = pos[bads_idx_pos] - origin
logger.info(f"Computing interpolation matrix from {len(pos_good)} sensor positions")
interpolation = _make_interpolation_matrix(pos_good, pos_bad)
logger.info(f"Interpolating {len(pos_bad)} sensors")
_do_interp_dots(inst, interpolation, goods_idx, bads_idx)
@verbose
def _interpolate_bads_ecog(inst, origin, exclude=None, verbose=None):
_interpolate_bads_eeg(inst, origin, exclude=exclude, ecog=True, verbose=verbose)
def _interpolate_bads_meg(
inst, mode="accurate", origin=(0.0, 0.0, 0.04), verbose=None, ref_meg=False
):
return _interpolate_bads_meeg(
inst, mode, origin, ref_meg=ref_meg, eeg=False, verbose=verbose
)
@verbose
def _interpolate_bads_nan(
inst,
ch_type,
ref_meg=False,
exclude=(),
*,
verbose=None,
):
info = _simplify_info(inst.info)
picks_type = pick_types(info, ref_meg=ref_meg, exclude=exclude, **{ch_type: True})
use_ch_names = [inst.info["ch_names"][p] for p in picks_type]
bads_type = [ch for ch in inst.info["bads"] if ch in use_ch_names]
if len(bads_type) == 0 or len(picks_type) == 0:
return
# select the bad channels to be interpolated
picks_bad = pick_channels(inst.info["ch_names"], bads_type, exclude=[])
inst._data[..., picks_bad, :] = np.nan
@verbose
def _interpolate_bads_meeg(
inst,
mode="accurate",
origin=(0.0, 0.0, 0.04),
meg=True,
eeg=True,
ref_meg=False,
exclude=(),
*,
method=None,
verbose=None,
):
from ..forward import _map_meg_or_eeg_channels
if method is None:
method = {"meg": "MNE", "eeg": "MNE"}
bools = dict(meg=meg, eeg=eeg)
info = _simplify_info(inst.info)
for ch_type, do in bools.items():
if not do:
continue
kw = dict(meg=False, eeg=False)
kw[ch_type] = True
picks_type = pick_types(info, ref_meg=ref_meg, exclude=exclude, **kw)
picks_good = pick_types(info, ref_meg=ref_meg, exclude="bads", **kw)
use_ch_names = [inst.info["ch_names"][p] for p in picks_type]
bads_type = [ch for ch in inst.info["bads"] if ch in use_ch_names]
if len(bads_type) == 0 or len(picks_type) == 0:
continue
# select the bad channels to be interpolated
picks_bad = pick_channels(inst.info["ch_names"], bads_type, exclude=[])
# do MNE based interpolation
if ch_type == "eeg":
picks_to = picks_type
bad_sel = np.isin(picks_type, picks_bad)
else:
picks_to = picks_bad
bad_sel = slice(None)
info_from = pick_info(inst.info, picks_good)
info_to = pick_info(inst.info, picks_to)
mapping = _map_meg_or_eeg_channels(info_from, info_to, mode=mode, origin=origin)
mapping = mapping[bad_sel]
_do_interp_dots(inst, mapping, picks_good, picks_bad)
@verbose
def _interpolate_bads_nirs(inst, exclude=(), verbose=None):
from mne.preprocessing.nirs import _validate_nirs_info
if len(pick_types(inst.info, fnirs=True, exclude=())) == 0:
return
# Returns pick of all nirs and ensures channels are correctly ordered
picks_nirs = _validate_nirs_info(inst.info)
nirs_ch_names = [inst.info["ch_names"][p] for p in picks_nirs]
nirs_ch_names = [ch for ch in nirs_ch_names if ch not in exclude]
bads_nirs = [ch for ch in inst.info["bads"] if ch in nirs_ch_names]
if len(bads_nirs) == 0:
return
picks_bad = pick_channels(inst.info["ch_names"], bads_nirs, exclude=[])
bads_mask = [p in picks_bad for p in picks_nirs]
chs = [inst.info["chs"][i] for i in picks_nirs]
locs3d = np.array([ch["loc"][:3] for ch in chs])
dist = pdist(locs3d)
dist = squareform(dist)
for bad in picks_bad:
dists_to_bad = dist[bad]
# Ignore distances to self
dists_to_bad[dists_to_bad == 0] = np.inf
# Ignore distances to other bad channels
dists_to_bad[bads_mask] = np.inf
# Find closest remaining channels for same frequency
closest_idx = np.argmin(dists_to_bad) + (bad % 2)
inst._data[bad] = inst._data[closest_idx]
# TODO: this seems like a bug because it does not respect reset_bads
inst.info["bads"] = [ch for ch in inst.info["bads"] if ch in exclude]
return inst
def _find_seeg_electrode_shaft(pos, tol_shaft=0.002, tol_spacing=1):
# 1) find nearest neighbor to define the electrode shaft line
# 2) find all contacts on the same line
# 3) remove contacts with large distances
dist = squareform(pdist(pos))
np.fill_diagonal(dist, np.inf)
shafts = list()
shaft_ts = list()
for i, n1 in enumerate(pos):
if any([i in shaft for shaft in shafts]):
continue
n2 = pos[np.argmin(dist[i])] # 1
# https://mathworld.wolfram.com/Point-LineDistance3-Dimensional.html
shaft_dists = np.linalg.norm(
np.cross((pos - n1), (pos - n2)), axis=1
) / np.linalg.norm(n2 - n1)
shaft = np.where(shaft_dists < tol_shaft)[0] # 2
shaft_prev = None
for _ in range(10): # avoid potential cycles
if np.array_equal(shaft, shaft_prev):
break
shaft_prev = shaft
# compute median shaft line
v = np.median(
[
pos[i] - pos[j]
for idx, i in enumerate(shaft)
for j in shaft[idx + 1 :]
],
axis=0,
)
c = np.median(pos[shaft], axis=0)
# recompute distances
shaft_dists = np.linalg.norm(
np.cross((pos - c), (pos - c + v)), axis=1
) / np.linalg.norm(v)
shaft = np.where(shaft_dists < tol_shaft)[0]
ts = np.array([np.dot(c - n0, v) / np.linalg.norm(v) ** 2 for n0 in pos[shaft]])
shaft_order = np.argsort(ts)
shaft = shaft[shaft_order]
ts = ts[shaft_order]
# only include the largest group with spacing with the error tolerance
# avoid interpolating across spans between contacts
t_diffs = np.diff(ts)
t_diff_med = np.median(t_diffs)
spacing_errors = (t_diffs - t_diff_med) / t_diff_med
groups = list()
group = [shaft[0]]
for j in range(len(shaft) - 1):
if spacing_errors[j] > tol_spacing:
groups.append(group)
group = [shaft[j + 1]]
else:
group.append(shaft[j + 1])
groups.append(group)
group = [group for group in groups if i in group][0]
ts = ts[np.isin(shaft, group)]
shaft = np.array(group, dtype=int)
shafts.append(shaft)
shaft_ts.append(ts)
return shafts, shaft_ts
@verbose
def _interpolate_bads_seeg(
inst, exclude=None, tol_shaft=0.002, tol_spacing=1, verbose=None
):
if exclude is None:
exclude = list()
picks = pick_types(inst.info, meg=False, seeg=True, exclude=exclude)
inst.info._check_consistency()
bads_idx = np.isin(np.array(inst.ch_names)[picks], inst.info["bads"])
if len(picks) == 0 or bads_idx.sum() == 0:
return
pos = inst._get_channel_positions(picks)
# Make sure only sEEG are used
bads_idx_pos = bads_idx[picks]
shafts, shaft_ts = _find_seeg_electrode_shaft(
pos, tol_shaft=tol_shaft, tol_spacing=tol_spacing
)
# interpolate the bad contacts
picks_bad = list(np.where(bads_idx_pos)[0])
for shaft, ts in zip(shafts, shaft_ts):
bads_shaft = np.array([idx for idx in picks_bad if idx in shaft])
if bads_shaft.size == 0:
continue
goods_shaft = shaft[np.isin(shaft, bads_shaft, invert=True)]
if goods_shaft.size < 4: # cubic spline requires 3 channels
msg = "No shaft" if shaft.size < 4 else "Not enough good channels"
no_shaft_chs = " and ".join(np.array(inst.ch_names)[bads_shaft])
raise RuntimeError(
f"{msg} found in a line with {no_shaft_chs} "
"at least 3 good channels on the same line "
f"are required for interpolation, {goods_shaft.size} found. "
f"Dropping {no_shaft_chs} is recommended."
)
logger.debug(
f"Interpolating {np.array(inst.ch_names)[bads_shaft]} using "
f"data from {np.array(inst.ch_names)[goods_shaft]}"
)
bads_shaft_idx = np.where(np.isin(shaft, bads_shaft))[0]
goods_shaft_idx = np.where(~np.isin(shaft, bads_shaft))[0]
z = inst._data[..., goods_shaft, :]
is_epochs = z.ndim == 3
if is_epochs:
z = z.swapaxes(0, 1)
z = z.reshape(z.shape[0], -1)
y = np.arange(z.shape[-1])
out = RectBivariateSpline(x=ts[goods_shaft_idx], y=y, z=z)(
x=ts[bads_shaft_idx], y=y
)
if is_epochs:
out = out.reshape(bads_shaft.size, inst._data.shape[0], -1)
out = out.swapaxes(0, 1)
inst._data[..., bads_shaft, :] = out
mne/channels/layout.py
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mne/channels/montage.py
+1846
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