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:orphan:
.. _glossary:
Glossary
========
.. currentmodule:: mne
The Glossary provides short definitions of MNE-Python-specific vocabulary and
general neuroimaging concepts. If you think a term is missing, please consider
`creating a new issue`_ or `opening a pull request`_ to add it.
.. glossary::
annotations
An annotation is defined by an onset, a duration, and a string
description. It can contain information about the experiments, but
also details on signals marked by a human: bad data segments,
sleep scores, sleep events (spindles, K-complex) etc.
An :class:`Annotations` object is a container of multiple annotations.
See :class:`Annotations` page for the API of the corresponding
object class and :ref:`tut-annotations`
for a tutorial on how to manipulate such objects.
BEM
BEM is the acronym for boundary element method or boundary element
model. Both are related to the forward model computation and more
specifically the definion of the conductor model. The
boundary element model consists of surfaces such as the inner skull,
outer skull and outer skin (a.k.a. scalp) that define compartments
of tissues of the head. You can compute the BEM surfaces with
:func:`mne.bem.make_watershed_bem` or :func:`mne.bem.make_flash_bem`.
See :ref:`tut-forward` for usage demo.
channels
Channels refer to MEG sensors, EEG electrodes or any extra electrode
or sensor such as EOG, ECG or sEEG, ECoG etc. Channels usually have
a type, such as gradiometer, and a unit, such as Tesla/Meter that
is used in the code base, e.g. for plotting.
dipole
See :term:`equivalent current dipole`.
epochs
Epochs (sometimes called "trials" in other software packages) are
equal-length spans of data extracted from raw continuous data. Usually,
epochs are extracted around stimulus events or subject responses,
though sometimes sequential or overlapping epochs are extracted (e.g.,
for analysis of resting-state activity). See :class:`Epochs` for the
API of the corresponding object class, and :ref:`tut-epochs-class` for
a narrative overview.
equivalent current dipole
An equivalent current dipole (ECD) is an approximate representation of
post-synaptic activity in a small region of cortex. The intracellular
currents that give rise to measurable EEG/MEG signals are thought to
originate in populations of cortical pyramidal neurons aligned
perpendicularly to the cortical surface. Because the length of such
current sources is very small relative to the distance between the
cortex and the EEG/MEG sensors, the fields measured by the techniques
are well-approximated by (i.e., "equivalent" to) fields generated by
idealized point sources (dipoles) located on the cortical surface.
events
Events correspond to specific time points in raw data; e.g.,
triggers, experimental condition events, etc. MNE represents events with
integers that are stored in numpy arrays of shape (n_events, 3). Such arrays
are classically obtained from a trigger channel, also referred to as
stim channel.
evoked
Evoked data are obtained by averaging epochs. Typically, an evoked object
is constructed for each subject and each condition, but it can also be
obtained by averaging a list of evoked over different subjects.
See :class:`EvokedArray` for the API of the corresponding
object class, and :ref:`tut-evoked-class` for a narrative overview.
fiducial point
There are three fiducial (a.k.a. cardinal) points: the left
preauricular point (LPA), the right preauricular point (RPA)
and the nasion.
first_samp
The :attr:`~mne.io.Raw.first_samp` attribute of :class:`~mne.io.Raw`
objects is an integer representing the number of time samples that
passed between the onset of the hardware acquisition system and the
time when data started to be recorded to disk. This approach to sample
numbering is a peculiarity of VectorView MEG systems, but for
consistency it is present in all :class:`~mne.io.Raw` objects
regardless of the source of the data. In other words,
:attr:`~mne.io.Raw.first_samp` will be ``0`` in :class:`~mne.io.Raw`
objects loaded from non-VectorView data files.
forward solution
The forward solution (abbr. ``fwd``) is a linear operator capturing the
relationship between each dipole location in the :term:`source space`
and the corresponding field distribution measured by the sensors (AKA,
the "lead field matrix"). Calculating a forward solution requires a
conductivity model of the head, encapsulating the geometry and
electrical conductivity of the different tissue compartments (see
:term:`boundary element model <BEM>` and
:class:`mne.bem.ConductorModel`).
GFP
Global Field Power (abbr. ``GFP``) is a measure of the (non-)uniformity
of the electromagnetic field at the sensors. It is typically calculated
as the standard deviation of the sensor values at each time point; thus
it is a one-dimensional time series capturing the spatial variability
of the signal across sensor locations.
HPI
Head position indicators (abbr. ``HPI``, or sometimes ``cHPI`` for
*continuous* head position indicators) are small coils attached to a
subject's head during MEG acquisition. Each coil emits a sinusoidal
signal of a different frequency, which is picked up by the MEG sensors
and can be used to infer the head position. With cHPI, the sinusoidal
signals are typically set at frequencies above any neural signal of
interest, and thus can be removed after head position correction via
low-pass filtering.
info
Also called ``measurement info``, it is a collection of metadata regarding
a Raw, Epochs or Evoked object; e.g.,
channel locations and types, sampling frequency,
preprocessing history such as filters ...
See :ref:`tut-info-class` for a narrative overview.
inverse operator
The inverse operator is an :math:`M \times N` matrix (:math:`M` source
locations by :math:`N` sensors) that, when applied to the sensor
signals, yields estimates of the brain activity that gave rise to the
observed sensor signals. Inverse operators are available for the linear
inverse methods MNE, dSPM, sLORETA and eLORETA.
label
A :class:`Label` refers to a region in the cortex, also often called
a region of interest (ROI) in the literature.
layout
A :class:`Layout <mne.channels.Layout>` gives sensor positions in 2
dimensions (defined by ``x``, ``y``, ``width``, and ``height`` values for
each sensor). It is primarily used for illustrative purposes (i.e., making
diagrams of approximate sensor positions in top-down diagrams of the head,
so-called topographies or topomaps).
montage
EEG channel names and the relative positions of the sensor w.r.t. the scalp.
While layout are 2D locations, montages give 3D locations. A montage
can also contain locations for HPI points, fiducial points, or
extra head shape points.
See :class:`~channels.DigMontage` for the API of the corresponding object
class.
morphing
Morphing refers to the operation of transferring source estimates from
one anatomy to another. It is commonly referred as realignment in fMRI
literature. This operation is necessary for group studies (to get the
data in a common space for statistical analysis).
See :ref:`ch_morph` for more details.
pick
An integer that is the index of a channel in the measurement info.
It allows to obtain the information on a channel in the list of channels
available in ``info['chs']``.
projector
A projector (abbr. ``proj``), also referred to as Signal Space
Projection (SSP), defines
a linear operation applied spatially to EEG or MEG data. You can see
this as a matrix multiplication that reduces the rank of the data by
projecting it to a lower dimensional subspace. Such a projection
operator is applied to both the data and the forward operator for
source localization. Note that EEG average referencing can be done
using such a projection operator. It is stored in the measurement
info in ``info['projs']``.
raw
It corresponds to continuous data (preprocessed or not). One typically
manipulates raw data when reading recordings in a file on disk.
See :class:`~io.RawArray` for the API of the corresponding
object class, and :ref:`tut-raw-class` for a narrative overview.
selection (abbr. sel)
A set of picks. E.g., all sensors included in a Region of Interest.
source estimates (abbr. ``stc``)
Source estimates, commonly referred to as STC (Source Time Courses),
are obtained from source localization methods,
such as dSPM, sLORETA, LCMV or MxNE.
It contains the amplitudes of the sources over time.
An STC object only stores the amplitudes of activations but
not the locations of the sources. To get access to the locations
you need to have the source space used to compute the forward
operator.
See :class:`SourceEstimate`, :class:`VolSourceEstimate`
:class:`VectorSourceEstimate`, :class:`MixedSourceEstimate`,
for the API of the corresponding object classes.
source space
A source space (abbr. ``src``) specifies where in the brain one wants
to estimate the
source amplitudes. It corresponds to locations of a set of
candidate equivalent current dipoles (ECD). MNE mostly works
with source spaces defined on the cortical surfaces estimated
by FreeSurfer from a T1-weighted MRI image. See
:ref:`tut-forward` to read on
how to compute a forward operator on a source space.
See :class:`SourceSpaces` for the API of the corresponding
object class.
stim channel
A stim channel, a.k.a. trigger channel, is a channel that encodes
events during the recording. It is typically a channel that is usually
zero and takes positive values when something happens (such as the
onset of a stimulus, or a subject response). Stim channels are often
prefixed with ``STI`` to distinguish them from other channel types. See
:ref:`stim-channel-defined` for more details.
trans
A coordinate frame affine transformation, usually between the Neuromag head
coordinate frame and the MRI Surface RAS coordinate frame used by Freesurfer.
.. LINKS
.. _`creating a new issue`:
https://github.com/mne-tools/mne-python/issues/new?template=glossary.md
.. _`opening a pull request`:
https://github.com/mne-tools/mne-python/pull/new/master
|
