<commit_before>Glossary
========

.. currentmodule:: mne

The Glossary provides short definitions of vocabulary specific to MNE-Python 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 textual
        description. It can contain information about the experiment, but
        also details on signals marked by a human such as bad data segments,
        sleep stages, sleep events (spindles, K-complex), and so on.
        An :class:`Annotations` object is a container for multiple annotations,
        which is available as the ``annotations`` attribute of :class:`~io.Raw`
        objects. See :class:`Annotations` for the class definition and
        :ref:`tut-events-vs-annotations` for a short tutorial.
        See also :term:`events`.

    array-like
        Something that acts like – or can be converted to – a
        :class:`NumPy array <numpy.ndarray>`.
        This includes (but is not limited to)
        :class:`arrays <numpy.ndarray>`, `lists <list>`, and
        `tuples <tuple>`.

    beamformer
        A beamformer is a popular source estimation approach that uses a set of
        spatial filters (beamformer weights) to compute time courses of sources
        at predefined locations. See :class:`beamformer.Beamformer` for the class
        definition. 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 definion of the conductor model in the
        forward model computation. The boundary element model consists of surfaces
        such as the inner skull, outer skull, and outer skin (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 a usage demo.

    channels
        Channels refer to MEG sensors, EEG electrodes or other sensors such as
        EOG, ECG, sEEG, ECoG, etc. Channels usually have
        a type (such as gradiometer), and a unit (such as T/m) used e.g. for
        plotting. See also :term:`data channels`.

    data channels
        Many functions in MNE-Python operate on "data channels" by default. These
        are channels that contain electrophysiological data from the brain,
        as opposed to other channel types such as EOG, ECG, stimulus/trigger,
        or acquisition system status data. The set of channels considered
        "data channels" in MNE contains the following types (together with scale
        factors for plotting):

        .. mne:: data channels list

    DICS
    dynamic imaging of coherent sources
        Dynamic Imaging of Coherent Sources is a method for computing source
        power in different frequency bands. See :ref:`ex-inverse-source-power`
        and :func:`beamformer.make_dics` for more details.

    digitization
        Digitization is a procedure of recording the head shape and locations of
        fiducial coils (or :term:`HPI`) and/or EEG electrodes on the head. They
        are represented as a set of points in 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 cortical region. 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 these 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 (dSPM) gives a noise-normalized
        minimum-norm estimate at a given source location. It 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
        like :term:`dSPM` and :term:`MNE`. sLORETA outputs
        standardized values (like dSPM), while eLORETA generates 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 segments of data extracted from continuous data. Usually,
        epochs are extracted around stimulus events or responses,
        though sometimes sequential or overlapping epochs are used (e.g.,
        for analysis of resting-state activity). See :class:`Epochs` for the
        class definition and :ref:`tut-epochs-class` for a narrative overview.

    events
        Events correspond to specific time points in raw data, such as triggers,
        experimental condition events, etc. MNE-Python represents events with
        integers stored in NumPy arrays of shape ``(n_events, 3)``. The first
        column contains the event onset (in samples) with :term:`first_samp`
        included. The last column contains the event code. The second
        column contains the signal value of the immediately preceding sample,
        and reflects the fact that event arrays sometimes originate from
        analog voltage channels ("trigger channels" or "stim channels"). In
        most cases, the second column is all zeros and can be ignored.
        Event arrays can be created with :func:`mne.make_fixed_length_events`,
        :func:`mne.read_events`, and :func:`mne.find_events`.
        See :ref:`tut-events-vs-annotations` for a short tutorial.
        See also :term:`events`.

    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 objects over different subjects.
        See :class:`EvokedArray` for the class definition and
        :ref:`tut-evoked-class` for a narrative overview.<commit_msg>DOC FIX circular reference in events (#10953)<commit_after>Glossary
========

.. currentmodule:: mne

The Glossary provides short definitions of vocabulary specific to MNE-Python 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 textual
        description. It can contain information about the experiment, but
        also details on signals marked by a human such as bad data segments,
        sleep stages, sleep events (spindles, K-complex), and so on.
        An :class:`Annotations` object is a container for multiple annotations,
        which is available as the ``annotations`` attribute of :class:`~io.Raw`
        objects. See :class:`Annotations` for the class definition and
        :ref:`tut-events-vs-annotations` for a short tutorial.
        See also :term:`events`.

    array-like
        Something that acts like – or can be converted to – a
        :class:`NumPy array <numpy.ndarray>`.
        This includes (but is not limited to)
        :class:`arrays <numpy.ndarray>`, `lists <list>`, and
        `tuples <tuple>`.

    beamformer
        A beamformer is a popular source estimation approach that uses a set of
        spatial filters (beamformer weights) to compute time courses of sources
        at predefined locations. See :class:`beamformer.Beamformer` for the class
        definition. 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 definion of the conductor model in the
        forward model computation. The boundary element model consists of surfaces
        such as the inner skull, outer skull, and outer skin (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 a usage demo.

    channels
        Channels refer to MEG sensors, EEG electrodes or other sensors such as
        EOG, ECG, sEEG, ECoG, etc. Channels usually have
        a type (such as gradiometer), and a unit (such as T/m) used e.g. for
        plotting. See also :term:`data channels`.

    data channels
        Many functions in MNE-Python operate on "data channels" by default. These
        are channels that contain electrophysiological data from the brain,
        as opposed to other channel types such as EOG, ECG, stimulus/trigger,
        or acquisition system status data. The set of channels considered
        "data channels" in MNE contains the following types (together with scale
        factors for plotting):

        .. mne:: data channels list

    DICS
    dynamic imaging of coherent sources
        Dynamic Imaging of Coherent Sources is a method for computing source
        power in different frequency bands. See :ref:`ex-inverse-source-power`
        and :func:`beamformer.make_dics` for more details.

    digitization
        Digitization is a procedure of recording the head shape and locations of
        fiducial coils (or :term:`HPI`) and/or EEG electrodes on the head. They
        are represented as a set of points in 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 cortical region. 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 these 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 (dSPM) gives a noise-normalized
        minimum-norm estimate at a given source location. It 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
        like :term:`dSPM` and :term:`MNE`. sLORETA outputs
        standardized values (like dSPM), while eLORETA generates 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 segments of data extracted from continuous data. Usually,
        epochs are extracted around stimulus events or responses,
        though sometimes sequential or overlapping epochs are used (e.g.,
        for analysis of resting-state activity). See :class:`Epochs` for the
        class definition and :ref:`tut-epochs-class` for a narrative overview.

    events
        Events correspond to specific time points in raw data, such as triggers,
        experimental condition events, etc. MNE-Python represents events with
        integers stored in NumPy arrays of shape ``(n_events, 3)``. The first
        column contains the event onset (in samples) with :term:`first_samp`
        included. The last column contains the event code. The second
        column contains the signal value of the immediately preceding sample,
        and reflects the fact that event arrays sometimes originate from
        analog voltage channels ("trigger channels" or "stim channels"). In
        most cases, the second column is all zeros and can be ignored.
        Event arrays can be created with :func:`mne.make_fixed_length_events`,
        :func:`mne.read_events`, and :func:`mne.find_events`.
        See :ref:`tut-events-vs-annotations` for a short tutorial.
        See also :term:`annotations`.

    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 objects over different subjects.
        See :class:`EvokedArray` for the class definition and
        :ref:`tut-evoked-class` for a narrative overview.
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