MNE-Python core terminology and general concepts


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 Annotations object is a container of multiple annotations. See Annotations page for the API of the corresponding object class and The Events and Annotations data structures for a tutorial on how to manipulate such objects.


Channels refer to MEG sensors, EEG electrodes or any extra electrode or sensor such as EOG, ECG or sEEG, ECoG etc. Channels have typically a type, such as gradiometer, and a unit, such as Tesla/Meter that is used in the code base, e.g. for plotting.


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 skiln (a.k.a. scalp) that define compartments of tissues of the head. You can compute the BEM surfaces with mne.bem.make_watershed_bem() or mne.bem.make_flash_bem(). See Head model and forward computation for usage demo.


See equivalent current dipole.

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.


Epochs are chunks of data extracted from raw continuous data. Typically, they correspond to the trials of an experimental design. See Epochs for the API of the corresponding object class, and The Epochs data structure: epoched data for a narrative overview.


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 EvokedArray for the API of the corresponding object class, and The Evoked data structure: evoked/averaged 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.


The attribute of raw objects called first_samp is an integer that refers to the number of time samples passed between the onset of the acquisition system and the time when data started to be written on disk. This is a specificity of the Vectorview MEG systems (fif files) but for consistency it is available for all file formats in MNE. One benefit of this system is that croppping data only boils down to a change of the first_samp attribute to know when cropped data was acquired.

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 (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 boundary element model and mne.bem.ConductorModel).


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 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.


A Label refers to a region in the cortex, also often called a region of interest (ROI) in the literature.


EEG channel names and the relative positions of the sensor w.r.t. the scalp. See Montage for 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. See Morphing source estimates: Moving data from one brain to another for more details.


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'].


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'].


It corresponds to continuous data (preprocessed or not). One typically manipulates raw data when reading recordings in a file on disk. See RawArray for the API of the corresponding object class, and The Raw data structure: continuous data for a narrative overview.

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 Head model and forward computation to read on how to compute a forward operator on a source space. See SourceSpaces for the API of the corresponding object class.

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 SourceEstimate, VolSourceEstimate VectorSourceEstimate, MixedSourceEstimate, for the API of the corresponding object classes.

selection (abbr. sel)

A set of picks. E.g., all sensors included in a Region of Interest.

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 always zero and that takes positive values when something happens such as the onset of a stimulus. Classical names for stim channels is STI 014 or STI 101. So-called events arrays are obtained from stim channels.


A coordinate frame affine transformation, usually between the Neuromag head coordinate frame and the MRI Surface RAS coordinate frame used by Freesurfer.