glossary.rst

orphan:

Glossary

.. currentmodule:: mne

MNE-Python core terminology and general concepts

.. 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.

    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.

    montage
        EEG channel names and the relative positions of the sensor w.r.t. the scalp.
        See :class:`~channels.Montage` 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.
        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.