.. 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::
:sorted:
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.
beamformer
Beamformer is a popular source estimation approach that uses a set of
spatial filters (beamformer weights) to compute time courses of sources
at predefined coordinates. See :class:`beamformer.Beamformer`. See
also :term:`LCMV`.
BEM
boundary element model
boundary element method
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:`bem.make_watershed_bem` or :func:`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. See also
:term:`data channels`.
data channels
Many functions in MNE operate by default on "data channels". These are
channels that typically hold *brain electophysiological* data,
as opposed to other forms of data, such as EOG, ECG, stimulus trigger,
or acquisition system status data. The set of channels considered
"data channels" in MNE is (along with their typical scale factors for
plotting, as they are stored in objects in SI units):
.. mne:: data channels list
DICS
dynamic imaging of coherent sources
Dynamic Imaging of Coherent Sources, a method for computing source
power in different frequency bands. see :ref:`ex-inverse-source-power`
and :func:`beamformer.make_dics`.
digitization
Digitization is a procedure of recording the headshape of a subject and
the fiducial coils (or :term:`HPI`) and/or eeg electrodes locations on
the subject’s head. They are represented as a set of points in a 3D space.
See :ref:`reading-dig-montages` and :ref:`dig-formats`.
dipole
ECD
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.
dSPM
dynamic statistical parametric mapping
Dynamic statistical parametric mapping (abbr. ``dSPM``) gives a noise-
normalized minimum-norm estimate at a given source location. dSPM is
calculated by dividing the activity estimate at each source location by
the baseline standard deviation of the noise.
eLORETA
sLORETA
eLORETA and sLORETA (exact and standardized low resolution brain
electromagnetic tomography) are linear source estimation techniques,
as are :term:`dSPM` and :term:`MNE`. sLORETA outputs
standardized values (like dSPM does), while eLORETA outputs normalized
current estimates. See :func:`minimum_norm.apply_inverse`,
:ref:`tut-inverse-methods`, and :ref:`example-sLORETA`.
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.
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
fiducial point
anatomical landmark
Fiducials are objects placed in the field of view of an imaging system
to act as a known spatial reference location that is easy to localize.
In neuroimaging, fiducials are often placed on anatomical landmarks
such as the nasion (NAS) or left/right preauricular points (LPA and
RPA).
These known reference locations are used to define a coordinate system
used for localization of sensors (hence NAS, LPA and RPA are often
called "cardinal points" because they define the cardinal directions of
the "head" coordinate system). The cardinal points are also useful when
co-registering measurements in different coordinate systems (such as
aligning EEG sensor locations to an MRI of the subject's head).
Due to the common neuroimaging practice of placing fiducial objects on
anatomical landmarks, the terms "fiducial", "anatomical landmark" and
"cardinal point" are often (erroneously) used interchangeably.
first_samp
The :attr:`~io.Raw.first_samp` attribute of :class:`~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:`~io.Raw` objects
regardless of the source of the data. In other words,
:attr:`~io.Raw.first_samp` will be ``0`` in :class:`~io.Raw`
objects loaded from non-VectorView data files.
forward
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 (A.K.A.,
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` and :class:`bem.ConductorModel`).
GFP
global field power
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.
HED
hierarchical event descriptors
Hierarchical event descriptors (abbr. ``HED``) are tags that use
keywords separated by '/' to describe different types of
experimental events (for example, stimulus/circle/red/left and
stimulus/circle/blue/left). These tags can be used to group
experimental events and select event types for analysis.
HPI
cHPI
head position indicator
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. See :ref:`tut-head-pos`.
info
Also called ``measurement info``, it is a collection of metadata
regarding a :class:`~io.Raw`, :class:`Epochs` or :class:`Evoked`
object, containing channel locations and types, sampling frequency,
preprocessing history such as filters, etc.
See :ref:`tut-info-class` for a narrative overview.
inverse
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.
See :func:`minimum_norm.apply_inverse`.
label
A :class:`Label` refers to a defined region in the cortex, also often called
a region of interest (ROI) in the literature. Labels can be defined
anatomically (based on physical structure of the cortex) or functionally
(based on cortical response to specific stimuli).
layout
A :class:`~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).
LCMV
LCMV beamformer
Linearly constrained minimum variance beamformer, which attempts to
estimate activity for a given source while suppressing cross-talk from
other regions, see :func:`beamformer.make_lcmv`. See also
:term:`beamformer`.
maximum intensity projection
A method of displaying activity within some volume by, for each pixel,
finding the maximum value along vector from the viewer to the pixel
(i.e., along the vector pependicular to the view plane).
MNE
minimum-norm estimate
minimum-norm estimation
Minimum-norm estimation (abbr. ``MNE``) can be used to generate a distributed
map of activation on a :term:`source space`, usually on a cortical surface.
MNE uses a linear :term:`inverse operator` to project sensor measurements
into the source space. The :term:`inverse operator` is computed from the
:term:`forward solution` for a subject and an estimate of the
:term:`noise covariance` of sensor measurements.
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.
noise covariance
Noise covariance is a matrix that contains the covariance between data
channels. It is a square matrix with shape ``n_channels`` :math:`\times`
``n_channels``. It is especially useful when working with multiple sensor
types (e.g. EEG and MEG). It is in
practice estimated from baseline periods or empty room measurements.
The matrix also provides a noise model that can be used for subsequent analysis
like source imaging.
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
SSP
A projector (abbr. ``proj``), also referred to as Signal Space
Projection (SSP), defines a linear operation applied spatially to EEG
or MEG data. A matrix multiplication of an SSP projector with the data
will reduce the rank of the data by projecting it to a
lower-dimensional subspace. Such projections are typically applied to
both the data and the forward operator when performing
source localization. Note that EEG average referencing can be done
using such a projection operator. Projectors are stored alongside data
in :term:`the measurement info<info>` in the field ``info['projs']``.
raw
`~io.Raw` objects hold 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.
ROI
region of interest
A spatial region where an experimental effect is expected to manifest.
This can be a collection of sensors or, when performing inverse imaging,
a set of vertices on the cortical surface or within the cortical volume.
See also :term:`label`.
selection
A selection is a set of picked channels (for example, all sensors
falling within a :term:`region of interest`).
STC
source estimate
source time course
Source estimates, commonly referred to as STC (Source Time Courses),
are obtained from source localization methods such as :term:`dSPM`,
:term:`sLORETA`, :term:`LCMV` or MxNE.
STCs contain the amplitudes of the neural sources over time.
In MNE-Python, :class:`SourceEstimate` objects only store the
amplitudes of activation but not the locations of the sources; the
locations are stored separately in the :class:`SourceSpaces` object
that was 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 :term:`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 about how to compute a forward operator on a source space.
See :class:`SourceSpaces` for the API of the corresponding
object class.
stim channel
trigger 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.
tfr
Time-frequency representation. This is often a spectrogram (STFT) or
scaleogram (wavelet), showing the frequency content as a function of
time.
trans
A coordinate frame affine transformation, usually between the Neuromag head
coordinate frame and the MRI Surface RAS coordinate frame used by Freesurfer.
whitening
A linear operation that transforms data with a known covariance
structure into "whitened data" which has a covariance structure that
is the identity matrix. In other words it creates virtual channels that
are uncorrelated and have unit variance. This is also known as a
sphering transformation.
The term "whitening" comes from the fact that light with a flat
frequency spectrum in the visible range is white, whereas
non-uniform frequency spectra lead to perception of different colors
(e.g., "pink noise" has a ``1/f`` characteristic, which for visible
light would appear pink).