<commit_before>: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.

    Beamformer
        Beamformer is a popular source estimation approach that uses a set of
        spatial filters (beamformer weights) to compute time courses of sources
        which coordinates are predefined. See :class:`mne.beamformer.Beamformer`.

    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.

    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
        See :term:`equivalent current dipole`.

    eLORETA and sLORETA
        eLORETA and sLORETA (exact and standardized low resolution brain
        electromagnetic tomography) are linear source estimation techniques,
        as are dSPM or :term:`MNE <minimum-norm estimation>`. sLORETA outputs
        standardized values (like dSPM does), while eLORETA outputs normalized
        current estimates. See :func:`mne.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.

    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. See :ref:`example-head-pos`.

    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.
        See :func:`mne.minimum_norm.apply_inverse`.

    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).

    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 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.

    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.
<commit_msg>DOC: adds noise covariance to glossary (#6875)<commit_after>: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.

    Beamformer
        Beamformer is a popular source estimation approach that uses a set of
        spatial filters (beamformer weights) to compute time courses of sources
        which coordinates are predefined. See :class:`mne.beamformer.Beamformer`.

    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.

    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
        See :term:`equivalent current dipole`.

    eLORETA and sLORETA
        eLORETA and sLORETA (exact and standardized low resolution brain
        electromagnetic tomography) are linear source estimation techniques,
        as are dSPM or :term:`MNE <minimum-norm estimation>`. sLORETA outputs
        standardized values (like dSPM does), while eLORETA outputs normalized
        current estimates. See :func:`mne.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.

    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. See :ref:`example-head-pos`.

    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.
        See :func:`mne.minimum_norm.apply_inverse`.

    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).

    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
        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.

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