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.
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
Annotationsobject is a container of multiple annotations. See
Annotationspage for the API of the corresponding object class and Function Annotations for a tutorial on how to manipulate such objects.
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
beamformer.Beamformer. See also LCMV.
- 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
bem.make_flash_bem(). See Head model and forward computation for usage demo.
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 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):
'mag': Magnetometers (scaled by 1e+15 to plot in fT)
'grad': Gradiometers (scaled by 1e+13 to plot in fT/cm)
'eeg': EEG (scaled by 1e+06 to plot in µV)
'csd': Current source density (scaled by 1000 to plot in mV/m²)
'seeg': sEEG (scaled by 1000 to plot in mV)
'ecog': ECoG (scaled by 1e+06 to plot in µV)
'dbs': DBS (scaled by 1e+06 to plot in µV)
'hbo': Oxyhemoglobin (scaled by 1e+06 to plot in µM)
'hbr': Deoxyhemoglobin (scaled by 1e+06 to plot in µM)
'fnirs_cw_amplitude': fNIRS (CW amplitude) (scaled by 1 to plot in V)
'fnirs_fd_ac_amplitude': fNIRS (FD AC amplitude) (scaled by 1 to plot in V)
'fnirs_fd_phase': fNIRS (FD phase) (scaled by 1 to plot in rad)
'fnirs_od': fNIRS (OD) (scaled by 1 to plot in V)
- dynamic imaging of coherent sources¶
Digitization is a procedure of recording the headshape of a subject and the fiducial coils (or HPI) and/or eeg electrodes locations on the subject’s head. They are represented as a set of points in a 3D space. See Reading sensor digitization files and Supported formats for digitized 3D locations.
- 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.
- 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 and sLORETA (exact and standardized low resolution brain electromagnetic tomography) are linear source estimation techniques, as are dSPM and MNE. sLORETA outputs standardized values (like dSPM does), while eLORETA outputs normalized current estimates. See
minimum_norm.apply_inverse(), Source localization with MNE/dSPM/sLORETA/eLORETA, and Compute sLORETA inverse solution on raw data.
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
Epochsfor the API of the corresponding object class, and The Epochs data structure: discontinuous data for a narrative overview.
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 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
EvokedArrayfor the API of the corresponding object class, and The Evoked data structure: evoked/averaged data for a narrative overview.
- 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.
Rawobjects 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
Rawobjects regardless of the source of the data. In other words,
Rawobjects 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 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 boundary element model and
- 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.
- head position indicator¶
Head position indicators (abbr.
HPI, or sometimes
cHPIfor 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 Extracting and visualizing subject head movement.
measurement info, it is a collection of metadata regarding a
Evokedobject, containing channel locations and types, sampling frequency, preprocessing history such as filters, etc. See The Info data structure for a narrative overview.
- inverse operator¶
The inverse operator is an \(M \times N\) matrix (\(M\) source locations by \(N\) 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
Labelrefers 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).
Layoutgives sensor positions in 2 dimensions (defined by
heightvalues 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 beamformer¶
Linearly constrained minimum variance beamformer, which attempts to estimate activity for a given source while suppressing cross-talk from other regions, see
beamformer.make_lcmv(). See also 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).
- minimum-norm estimate¶
- minimum-norm estimation¶
Minimum-norm estimation (abbr.
MNE) can be used to generate a distributed map of activation on a source space, usually on a cortical surface. MNE uses a linear inverse operator to project sensor measurements into the source space. The inverse operator is computed from the forward solution for a subject and an estimate of the noise covariance of sensor measurements.
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
DigMontagefor the API of the corresponding object class.
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 Morphing and averaging source estimates 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. 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.
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
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 the measurement info in the field
Rawobjects hold continuous data (preprocessed or not). One typically manipulates raw data when reading recordings in a file on disk. See
RawArrayfor the API of the corresponding object class, and The Raw data structure: continuous data for a narrative overview.
- 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 label.
A selection is a set of picked channels (for example, all sensors falling within a region of interest).
- 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. MNE mostly works with source spaces defined on the cortical surfaces estimated by FreeSurfer from a T1-weighted MRI image. See Head model and forward computation to read about how to compute a forward operator on a source space. See
SourceSpacesfor the API of the corresponding object class.
- source estimate¶
- source time course¶
Source estimates, commonly referred to as STC (Source Time Courses), are obtained from source localization methods such as dSPM, sLORETA, LCMV or MxNE. STCs contain the amplitudes of the neural sources over time. In MNE-Python,
SourceEstimateobjects only store the amplitudes of activation but not the locations of the sources; the locations are stored separately in the
SourceSpacesobject that was used to compute the forward operator. See
MixedSourceEstimate, for the API of the corresponding object classes.
- 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
STIto distinguish them from other channel types. See What is a STIM channel? for more details.
A coordinate frame affine transformation, usually between the Neuromag head coordinate frame and the MRI Surface RAS coordinate frame used by Freesurfer.
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/fcharacteristic, which for visible light would appear pink).