mne.Info#

class mne.Info(*args, **kwargs)[source]#

Measurement information.

This data structure behaves like a dictionary. It contains all metadata that is available for a recording. However, its keys are restricted to those provided by the FIF format specification, so new entries should not be manually added.

Warning

The only entries that should be manually changed by the user are info['bads'], info['description'], info['device_info'], info['dev_head_t'], info['experimenter'], info[‘helium_info’], info['line_freq'], info['temp'] and info['subject_info']. All other entries should be considered read-only, though they can be modified by various MNE-Python functions or methods (which have safeguards to ensure all fields remain in sync).

Warning

This class should not be instantiated directly. To create a measurement information structure, use mne.create_info().

Parameters:
*argslist

Arguments.

**kwargsdict

Keyword arguments.

See also

mne.create_info

Notes

The following parameters have a nested structure.

  • chs list of dict:

    calfloat

    The calibration factor to bring the channels to physical units. Used in product with range to scale the data read from disk.

    ch_namestr

    The channel name.

    coil_typeint

    Coil type, e.g. FIFFV_COIL_MEG.

    coord_frameint

    The coordinate frame used, e.g. FIFFV_COORD_HEAD.

    kindint

    The kind of channel, e.g. FIFFV_EEG_CH.

    locarray, shape (12,)

    Channel location. For MEG this is the position plus the normal given by a 3x3 rotation matrix. For EEG this is the position followed by reference position (with 6 unused). The values are specified in device coordinates for MEG and in head coordinates for EEG channels, respectively.

    lognoint

    Logical channel number, conventions in the usage of this number vary.

    rangefloat

    The hardware-oriented part of the calibration factor. This should be only applied to the continuous raw data. Used in product with cal to scale data read from disk.

    scannoint

    Scanning order number, starting from 1.

    unitint

    The unit to use, e.g. FIFF_UNIT_T_M.

    unit_mulint

    Unit multipliers, most commonly FIFF_UNITM_NONE.

  • comps list of dict:

    ctfkindint

    CTF compensation grade.

    colcalsndarray

    Column calibrations.

    matdict

    A named matrix dictionary (with entries “data”, “col_names”, etc.) containing the compensation matrix.

    rowcalsndarray

    Row calibrations.

    save_calibratedbool

    Were the compensation data saved in calibrated form.

  • device_info dict:

    typestr

    Device type.

    modelstr

    Device model.

    serialstr

    Device serial.

    sitestr

    Device site.

  • dig list of dict:

    kindint

    The kind of channel, e.g. FIFFV_POINT_EEG, FIFFV_POINT_CARDINAL.

    rarray, shape (3,)

    3D position in m. and coord_frame.

    identint

    Number specifying the identity of the point. e.g. FIFFV_POINT_NASION if kind is FIFFV_POINT_CARDINAL, or 42 if kind is FIFFV_POINT_EEG.

    coord_frameint

    The coordinate frame used, e.g. FIFFV_COORD_HEAD.

  • events list of dict:

    channelslist of int

    Channel indices for the events.

    listndarray, shape (n_events * 3,)

    Events in triplets as number of samples, before, after.

  • file_id dict:

    versionint

    FIF format version, i.e. FIFFC_VERSION.

    machidndarray, shape (2,)

    Unique machine ID, usually derived from the MAC address.

    secsint

    Time in seconds.

    usecsint

    Time in microseconds.

  • helium_info dict:

    he_level_rawfloat

    Helium level (%) before position correction.

    helium_levelfloat

    Helium level (%) after position correction.

    orig_file_guidstr

    Original file GUID.

    meas_datetuple of int

    The helium level meas date.

  • hpi_meas list of dict:

    creatorstr

    Program that did the measurement.

    sfreqfloat

    Sample rate.

    nchanint

    Number of channels used.

    naveint

    Number of averages used.

    ncoilint

    Number of coils used.

    first_sampint

    First sample used.

    last_sampint

    Last sample used.

    hpi_coilslist of dict

    Coils, containing:

    number: int

    Coil number

    epochndarray

    Buffer containing one epoch and channel.

    slopesndarray, shape (n_channels,)

    HPI data.

    corr_coeffndarray, shape (n_channels,)

    HPI curve fit correlations.

    coil_freqfloat

    HPI coil excitation frequency

  • hpi_results list of dict:

    dig_pointslist

    Digitization points (see dig definition) for the HPI coils.

    orderndarray, shape (ncoil,)

    The determined digitization order.

    usedndarray, shape (nused,)

    The indices of the used coils.

    momentsndarray, shape (ncoil, 3)

    The coil moments.

    goodnessndarray, shape (ncoil,)

    The goodness of fits.

    good_limitfloat

    The goodness of fit limit.

    dist_limitfloat

    The distance limit.

    acceptint

    Whether or not the fit was accepted.

    coord_transinstance of Transformation

    The resulting MEG<->head transformation.

  • hpi_subsystem dict:

    ncoilint

    The number of coils.

    event_channelstr

    The event channel used to encode cHPI status (e.g., STI201).

    hpi_coilslist of ndarray

    List of length ncoil, each 4-element ndarray contains the event bits used on the event channel to indicate cHPI status (using the first element of these arrays is typically sufficient).

  • mri_id dict:

    versionint

    FIF format version, i.e. FIFFC_VERSION.

    machidndarray, shape (2,)

    Unique machine ID, usually derived from the MAC address.

    secsint

    Time in seconds.

    usecsint

    Time in microseconds.

  • proc_history list of dict:

    block_iddict

    See id above.

    datendarray, shape (2,)

    2-element tuple of seconds and microseconds.

    experimenterstr

    Name of the person who ran the program.

    creatorstr

    Program that did the processing.

    max_infodict

    Maxwel filtering info, can contain:

    sss_infodict

    SSS processing information.

    max_st

    tSSS processing information.

    sss_ctcdict

    Cross-talk processing information.

    sss_caldict

    Fine-calibration information.

    smartshielddict

    MaxShield information. This dictionary is (always?) empty, but its presence implies that MaxShield was used during acquisition.

  • subject_info dict:

    idint

    Integer subject identifier.

    his_idstr

    String subject identifier.

    last_namestr

    Last name.

    first_namestr

    First name.

    middle_namestr

    Middle name.

    birthdaytuple of int

    Birthday in (year, month, day) format.

    sexint

    Subject sex (0=unknown, 1=male, 2=female).

    handint

    Handedness (1=right, 2=left, 3=ambidextrous).

    weightfloat

    Weight in kilograms.

    heightfloat

    Height in meters.

Attributes:
acq_parsstr | None

MEG system acquisition parameters. See mne.AcqParserFIF for details.

acq_stimstr | None

MEG system stimulus parameters.

badslist of str

List of bad (noisy/broken) channels, by name. These channels will by default be ignored by many processing steps.

ch_nameslist of str

The names of the channels.

chslist of dict

A list of channel information dictionaries, one per channel. See Notes for more information.

command_linestr

Contains the command and arguments used to create the source space (used for source estimation).

compslist of dict

CTF software gradient compensation data. See Notes for more information.

ctf_head_tTransform | None

The transformation from 4D/CTF head coordinates to Neuromag head coordinates. This is only present in 4D/CTF data.

custom_ref_appliedint

Whether a custom (=other than average) reference has been applied to the EEG data. This flag is checked by some algorithms that require an average reference to be set.

descriptionstr | None

String description of the recording.

dev_ctf_tTransform | None

The transformation from device coordinates to 4D/CTF head coordinates. This is only present in 4D/CTF data.

dev_head_tTransform | None

The device to head transformation.

device_infodict | None

Information about the acquisition device. See Notes for details.

New in version 0.19.

diglist of dict | None

The Polhemus digitization data in head coordinates. See Notes for more information.

eventslist of dict

Event list, sometimes extracted from the stim channels by Neuromag systems. In general this should not be used and mne.find_events() should be used for event processing. See Notes for more information.

experimenterstr | None

Name of the person that ran the experiment.

file_iddict | None

The FIF globally unique ID. See Notes for more information.

gantry_anglefloat | None

Tilt angle of the gantry in degrees.

helium_infodict | None

Information about the device helium. See Notes for details.

New in version 0.19.

highpassfloat

Highpass corner frequency in Hertz. Zero indicates a DC recording.

hpi_measlist of dict

HPI measurements that were taken at the start of the recording (e.g. coil frequencies). See Notes for details.

hpi_resultslist of dict

Head position indicator (HPI) digitization points and fit information (e.g., the resulting transform). See Notes for details.

hpi_subsystemdict | None

Information about the HPI subsystem that was used (e.g., event channel used for cHPI measurements). See Notes for details.

kit_system_idint

Identifies the KIT system.

line_freqfloat | None

Frequency of the power line in Hertz.

lowpassfloat

Lowpass corner frequency in Hertz. It is automatically set to half the sampling rate if there is otherwise no low-pass applied to the data.

maxshieldbool

True if active shielding (IAS) was active during recording.

meas_datedatetime

The time (UTC) of the recording.

Changed in version 0.20: This is stored as a datetime object instead of a tuple of seconds/microseconds.

meas_filestr | None

Raw measurement file (used for source estimation).

meas_iddict | None

The ID assigned to this measurement by the acquisition system or during file conversion. Follows the same format as file_id.

mri_filestr | None

File containing the MRI to head transformation (used for source estimation).

mri_head_tdict | None

Transformation from MRI to head coordinates (used for source estimation).

mri_iddict | None

MRI unique ID (used for source estimation).

nchanint

Number of channels.

proc_historylist of dict

The MaxFilter processing history. See Notes for details.

proj_idint | None

ID number of the project the experiment belongs to.

proj_namestr | None

Name of the project the experiment belongs to.

projslist of Projection

List of SSP operators that operate on the data. See mne.Projection for details.

sfreqfloat

Sampling frequency in Hertz.

subject_infodict | None

Information about the subject. See Notes for details.

tempobject | None

Can be used to store temporary objects in an Info instance. It will not survive an I/O roundtrip.

New in version 0.24.

utc_offsetstr

“UTC offset of related meas_date (sHH:MM).

New in version 0.19.

working_dirstr

Working directory used when the source space was created (used for source estimation).

xplotter_layoutstr

Layout of the Xplotter (Neuromag system only).

Methods

__contains__(key, /)

True if the dictionary has the specified key, else False.

__getitem__

x.__getitem__(y) <==> x[y]

__iter__(/)

Implement iter(self).

__len__(/)

Return len(self).

clear()

copy()

Copy the instance.

fromkeys(iterable[, value])

Create a new dictionary with keys from iterable and values set to value.

get(key[, default])

Return the value for key if key is in the dictionary, else default.

get_channel_types([picks, unique, only_data_chs])

Get a list of channel type for each channel.

get_montage()

Get a DigMontage from instance.

items()

keys()

normalize_proj()

(Re-)Normalize projection vectors after subselection.

pop(key[, default])

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem(/)

Remove and return a (key, value) pair as a 2-tuple.

set_montage(montage[, match_case, ...])

Set EEG/sEEG/ECoG/DBS/fNIRS channel positions and digitization points.

setdefault(key[, default])

Insert key with a value of default if key is not in the dictionary.

update([other])

Update method using __setitem__().

values()

__contains__(key, /)#

True if the dictionary has the specified key, else False.

__getitem__()#

x.__getitem__(y) <==> x[y]

__iter__(/)#

Implement iter(self).

__len__(/)#

Return len(self).

clear() None.  Remove all items from D.#
property compensation_grade#

The current gradient compensation grade.

copy()[source]#

Copy the instance.

Returns:
infoinstance of Info

The copied info.

fromkeys(iterable, value=None, /)#

Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)#

Return the value for key if key is in the dictionary, else default.

get_channel_types(picks=None, unique=False, only_data_chs=False)[source]#

Get a list of channel type for each channel.

Parameters:
picksstr | array_like | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

uniquebool

Whether to return only unique channel types. Default is False.

only_data_chsbool

Whether to ignore non-data channels. Default is False.

Returns:
channel_typeslist

The channel types.

get_montage()[source]#

Get a DigMontage from instance.

Returns:
montageNone | str | DigMontage

A montage containing channel positions. If a string or DigMontage is specified, the existing channel information will be updated with the channel positions from the montage. Valid strings are the names of the built-in montages that ship with MNE-Python; you can list those via mne.channels.get_builtin_montages(). If None (default), the channel positions will be removed from the Info.

items() a set-like object providing a view on D's items#
keys() a set-like object providing a view on D's keys#
normalize_proj()[source]#

(Re-)Normalize projection vectors after subselection.

Applying projection after sub-selecting a set of channels that were originally used to compute the original projection vectors can be dangerous (e.g., if few channels remain, most power was in channels that are no longer picked, etc.). By default, mne will emit a warning when this is done.

This function will re-normalize projectors to use only the remaining channels, thus avoiding that warning. Only use this function if you’re confident that the projection vectors still adequately capture the original signal of interest.

Examples using normalize_proj:

Computing source timecourses with an XFit-like multi-dipole model

Computing source timecourses with an XFit-like multi-dipole model

Computing source timecourses with an XFit-like multi-dipole model
pop(key, default=<unrepresentable>, /)#

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem(/)#

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

set_montage(montage, match_case=True, match_alias=False, on_missing='raise', verbose=None)[source]#

Set EEG/sEEG/ECoG/DBS/fNIRS channel positions and digitization points.

Parameters:
montageNone | str | DigMontage

A montage containing channel positions. If a string or DigMontage is specified, the existing channel information will be updated with the channel positions from the montage. Valid strings are the names of the built-in montages that ship with MNE-Python; you can list those via mne.channels.get_builtin_montages(). If None (default), the channel positions will be removed from the Info.

match_casebool

If True (default), channel name matching will be case sensitive.

New in version 0.20.

match_aliasbool | dict

Whether to use a lookup table to match unrecognized channel location names to their known aliases. If True, uses the mapping in mne.io.constants.CHANNEL_LOC_ALIASES. If a dict is passed, it will be used instead, and should map from non-standard channel names to names in the specified montage. Default is False.

New in version 0.23.

on_missing‘raise’ | ‘warn’ | ‘ignore’

Can be 'raise' (default) to raise an error, 'warn' to emit a warning, or 'ignore' to ignore when channels have missing coordinates.

New in version 0.20.1.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:
instinstance of Raw | Epochs | Evoked

The instance, modified in-place.

Notes

Warning

Only EEG/sEEG/ECoG/DBS/fNIRS channels can have their positions set using a montage. Other channel types (e.g., MEG channels) should have their positions defined properly using their data reading functions.

Examples using set_montage:

Creating MNE-Python data structures from scratch

Creating MNE-Python data structures from scratch

Creating MNE-Python data structures from scratch
XDAWN Decoding From EEG data

XDAWN Decoding From EEG data

XDAWN Decoding From EEG data
setdefault(key, default=None, /)#

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update(other=None, **kwargs)[source]#

Update method using __setitem__().

values() an object providing a view on D's values#

Examples using mne.Info#

Modifying data in-place

Modifying data in-place

Modifying data in-place
Parsing events from raw data

Parsing events from raw data

Parsing events from raw data
The Info data structure

The Info data structure

The Info data structure
Working with sensor locations

Working with sensor locations

Working with sensor locations
Configuring MNE-Python

Configuring MNE-Python

Configuring MNE-Python
Getting started with mne.Report

Getting started with mne.Report

Getting started with mne.Report
Importing data from fNIRS devices

Importing data from fNIRS devices

Importing data from fNIRS devices
Working with CTF data: the Brainstorm auditory dataset

Working with CTF data: the Brainstorm auditory dataset

Working with CTF data: the Brainstorm auditory dataset
The Raw data structure: continuous data

The Raw data structure: continuous data

The Raw data structure: continuous data
Working with events

Working with events

Working with events
Annotating continuous data

Annotating continuous data

Annotating continuous data
Overview of artifact detection

Overview of artifact detection

Overview of artifact detection
Handling bad channels

Handling bad channels

Handling bad channels
Rejecting bad data spans and breaks

Rejecting bad data spans and breaks

Rejecting bad data spans and breaks
Filtering and resampling data

Filtering and resampling data

Filtering and resampling data
Repairing artifacts with regression

Repairing artifacts with regression

Repairing artifacts with regression
Background on projectors and projections

Background on projectors and projections

Background on projectors and projections
Repairing artifacts with SSP

Repairing artifacts with SSP

Repairing artifacts with SSP
Setting the EEG reference

Setting the EEG reference

Setting the EEG reference
Extracting and visualizing subject head movement

Extracting and visualizing subject head movement

Extracting and visualizing subject head movement
Signal-space separation (SSS) and Maxwell filtering

Signal-space separation (SSS) and Maxwell filtering

Signal-space separation (SSS) and Maxwell filtering
Preprocessing functional near-infrared spectroscopy (fNIRS) data

Preprocessing functional near-infrared spectroscopy (fNIRS) data

Preprocessing functional near-infrared spectroscopy (fNIRS) data
Visualizing epoched data

Visualizing epoched data

Visualizing epoched data
Auto-generating Epochs metadata

Auto-generating Epochs metadata

Auto-generating Epochs metadata
Visualizing Evoked data

Visualizing Evoked data

Visualizing Evoked data
EEG analysis - Event-Related Potentials (ERPs)

EEG analysis - Event-Related Potentials (ERPs)

EEG analysis - Event-Related Potentials (ERPs)
Frequency and time-frequency sensor analysis

Frequency and time-frequency sensor analysis

Frequency and time-frequency sensor analysis
Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset

Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset

Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset
Source alignment and coordinate frames

Source alignment and coordinate frames

Source alignment and coordinate frames
Using an automated approach to coregistration

Using an automated approach to coregistration

Using an automated approach to coregistration
Head model and forward computation

Head model and forward computation

Head model and forward computation
EEG forward operator with a template MRI

EEG forward operator with a template MRI

EEG forward operator with a template MRI
How MNE uses FreeSurfer's outputs

How MNE uses FreeSurfer’s outputs

How MNE uses FreeSurfer's outputs
Computing a covariance matrix

Computing a covariance matrix

Computing a covariance matrix
Source localization with equivalent current dipole (ECD) fit

Source localization with equivalent current dipole (ECD) fit

Source localization with equivalent current dipole (ECD) fit
Source localization with MNE, dSPM, sLORETA, and eLORETA

Source localization with MNE, dSPM, sLORETA, and eLORETA

Source localization with MNE, dSPM, sLORETA, and eLORETA
The role of dipole orientations in distributed source localization

The role of dipole orientations in distributed source localization

The role of dipole orientations in distributed source localization
Computing various MNE solutions

Computing various MNE solutions

Computing various MNE solutions
Source reconstruction using an LCMV beamformer

Source reconstruction using an LCMV beamformer

Source reconstruction using an LCMV beamformer
EEG source localization given electrode locations on an MRI

EEG source localization given electrode locations on an MRI

EEG source localization given electrode locations on an MRI
Brainstorm Elekta phantom dataset tutorial

Brainstorm Elekta phantom dataset tutorial

Brainstorm Elekta phantom dataset tutorial
4D Neuroimaging/BTi phantom dataset tutorial

4D Neuroimaging/BTi phantom dataset tutorial

4D Neuroimaging/BTi phantom dataset tutorial
Visualising statistical significance thresholds on EEG data

Visualising statistical significance thresholds on EEG data

Visualising statistical significance thresholds on EEG data
Non-parametric 1 sample cluster statistic on single trial power

Non-parametric 1 sample cluster statistic on single trial power

Non-parametric 1 sample cluster statistic on single trial power
Non-parametric between conditions cluster statistic on single trial power

Non-parametric between conditions cluster statistic on single trial power

Non-parametric between conditions cluster statistic on single trial power
Mass-univariate twoway repeated measures ANOVA on single trial power

Mass-univariate twoway repeated measures ANOVA on single trial power

Mass-univariate twoway repeated measures ANOVA on single trial power
Spatiotemporal permutation F-test on full sensor data

Spatiotemporal permutation F-test on full sensor data

Spatiotemporal permutation F-test on full sensor data
Permutation t-test on source data with spatio-temporal clustering

Permutation t-test on source data with spatio-temporal clustering

Permutation t-test on source data with spatio-temporal clustering
Repeated measures ANOVA on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering
Decoding (MVPA)

Decoding (MVPA)

Decoding (MVPA)
Locating intracranial electrode contacts

Locating intracranial electrode contacts

Locating intracranial electrode contacts
Working with sEEG data

Working with sEEG data

Working with sEEG data
Working with ECoG data

Working with ECoG data

Working with ECoG data
Sleep stage classification from polysomnography (PSG) data

Sleep stage classification from polysomnography (PSG) data

Sleep stage classification from polysomnography (PSG) data
Creating MNE-Python data structures from scratch

Creating MNE-Python data structures from scratch

Creating MNE-Python data structures from scratch
Corrupt known signal with point spread

Corrupt known signal with point spread

Corrupt known signal with point spread
DICS for power mapping

DICS for power mapping

DICS for power mapping
How to use data in neural ensemble (NEO) format

How to use data in neural ensemble (NEO) format

How to use data in neural ensemble (NEO) format
Reading/Writing a noise covariance matrix

Reading/Writing a noise covariance matrix

Reading/Writing a noise covariance matrix
Reading XDF EEG data

Reading XDF EEG data

Reading XDF EEG data
Compare simulated and estimated source activity

Compare simulated and estimated source activity

Compare simulated and estimated source activity
Generate simulated evoked data

Generate simulated evoked data

Generate simulated evoked data
Generate simulated raw data

Generate simulated raw data

Generate simulated raw data
Simulate raw data using subject anatomy

Simulate raw data using subject anatomy

Simulate raw data using subject anatomy
Generate simulated source data

Generate simulated source data

Generate simulated source data
Cortical Signal Suppression (CSS) for removal of cortical signals

Cortical Signal Suppression (CSS) for removal of cortical signals

Cortical Signal Suppression (CSS) for removal of cortical signals
Define target events based on time lag, plot evoked response

Define target events based on time lag, plot evoked response

Define target events based on time lag, plot evoked response
Identify EEG Electrodes Bridged by too much Gel

Identify EEG Electrodes Bridged by too much Gel

Identify EEG Electrodes Bridged by too much Gel
Transform EEG data using current source density (CSD)

Transform EEG data using current source density (CSD)

Transform EEG data using current source density (CSD)
Show EOG artifact timing

Show EOG artifact timing

Show EOG artifact timing
Find MEG reference channel artifacts

Find MEG reference channel artifacts

Find MEG reference channel artifacts
Visualise NIRS artifact correction methods

Visualise NIRS artifact correction methods

Visualise NIRS artifact correction methods
Maxwell filter data with movement compensation

Maxwell filter data with movement compensation

Maxwell filter data with movement compensation
Annotate movement artifacts and reestimate dev_head_t

Annotate movement artifacts and reestimate dev_head_t

Annotate movement artifacts and reestimate dev_head_t
Shifting time-scale in evoked data

Shifting time-scale in evoked data

Shifting time-scale in evoked data
XDAWN Denoising

XDAWN Denoising

XDAWN Denoising
How to convert 3D electrode positions to a 2D image

How to convert 3D electrode positions to a 2D image

How to convert 3D electrode positions to a 2D image
Plotting with ``mne.viz.Brain``

Plotting with mne.viz.Brain

Plotting with ``mne.viz.Brain``
Visualize channel over epochs as an image

Visualize channel over epochs as an image

Visualize channel over epochs as an image
Plotting EEG sensors on the scalp

Plotting EEG sensors on the scalp

Plotting EEG sensors on the scalp
Plotting topographic arrowmaps of evoked data

Plotting topographic arrowmaps of evoked data

Plotting topographic arrowmaps of evoked data
Whitening evoked data with a noise covariance

Whitening evoked data with a noise covariance

Whitening evoked data with a noise covariance
Plotting sensor layouts of MEG systems

Plotting sensor layouts of MEG systems

Plotting sensor layouts of MEG systems
Plot the MNE brain and helmet

Plot the MNE brain and helmet

Plot the MNE brain and helmet
Make figures more publication ready

Make figures more publication ready

Make figures more publication ready
Plot single trial activity, grouped by ROI and sorted by RT

Plot single trial activity, grouped by ROI and sorted by RT

Plot single trial activity, grouped by ROI and sorted by RT
Plot custom topographies for MEG sensors

Plot custom topographies for MEG sensors

Plot custom topographies for MEG sensors
Compute a cross-spectral density (CSD) matrix

Compute a cross-spectral density (CSD) matrix

Compute a cross-spectral density (CSD) matrix
Compute Power Spectral Density of inverse solution from single epochs

Compute Power Spectral Density of inverse solution from single epochs

Compute Power Spectral Density of inverse solution from single epochs
Compute power and phase lock in label of the source space

Compute power and phase lock in label of the source space

Compute power and phase lock in label of the source space
Compute source power spectral density (PSD) in a label

Compute source power spectral density (PSD) in a label

Compute source power spectral density (PSD) in a label
Compute source power spectral density (PSD) of VectorView and OPM data

Compute source power spectral density (PSD) of VectorView and OPM data

Compute source power spectral density (PSD) of VectorView and OPM data
Compute induced power in the source space with dSPM

Compute induced power in the source space with dSPM

Compute induced power in the source space with dSPM
Temporal whitening with AR model

Temporal whitening with AR model

Temporal whitening with AR model
Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert)

Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert)

Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert)
Permutation F-test on sensor data with 1D cluster level

Permutation F-test on sensor data with 1D cluster level

Permutation F-test on sensor data with 1D cluster level
FDR correction on T-test on sensor data

FDR correction on T-test on sensor data

FDR correction on T-test on sensor data
Regression on continuous data (rER[P/F])

Regression on continuous data (rER[P/F])

Regression on continuous data (rER[P/F])
Permutation T-test on sensor data

Permutation T-test on sensor data

Permutation T-test on sensor data
Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)

Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)

Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)
Decoding in time-frequency space using Common Spatial Patterns (CSP)

Decoding in time-frequency space using Common Spatial Patterns (CSP)

Decoding in time-frequency space using Common Spatial Patterns (CSP)
Decoding source space data

Decoding source space data

Decoding source space data
Continuous Target Decoding with SPoC

Continuous Target Decoding with SPoC

Continuous Target Decoding with SPoC
Decoding sensor space data with generalization across time and conditions

Decoding sensor space data with generalization across time and conditions

Decoding sensor space data with generalization across time and conditions
Analysis of evoked response using ICA and PCA reduction techniques

Analysis of evoked response using ICA and PCA reduction techniques

Analysis of evoked response using ICA and PCA reduction techniques
XDAWN Decoding From EEG data

XDAWN Decoding From EEG data

XDAWN Decoding From EEG data
Compute effect-matched-spatial filtering (EMS)

Compute effect-matched-spatial filtering (EMS)

Compute effect-matched-spatial filtering (EMS)
Linear classifier on sensor data with plot patterns and filters

Linear classifier on sensor data with plot patterns and filters

Linear classifier on sensor data with plot patterns and filters
Receptive Field Estimation and Prediction

Receptive Field Estimation and Prediction

Receptive Field Estimation and Prediction
Compute Spectro-Spatial Decomposition (SSD) spatial filters

Compute Spectro-Spatial Decomposition (SSD) spatial filters

Compute Spectro-Spatial Decomposition (SSD) spatial filters
Use source space morphing

Use source space morphing

Use source space morphing
Compute MNE-dSPM inverse solution on single epochs

Compute MNE-dSPM inverse solution on single epochs

Compute MNE-dSPM inverse solution on single epochs
Source localization with a custom inverse solver

Source localization with a custom inverse solver

Source localization with a custom inverse solver
Compute source power using DICS beamformer

Compute source power using DICS beamformer

Compute source power using DICS beamformer
Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM

Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM

Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM
Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method

Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method

Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method
Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute sparse inverse solution with mixed norm: MxNE and irMxNE
Compute MNE inverse solution on evoked data with a mixed source space

Compute MNE inverse solution on evoked data with a mixed source space

Compute MNE inverse solution on evoked data with a mixed source space
Compute source power estimate by projecting the covariance with MNE

Compute source power estimate by projecting the covariance with MNE

Compute source power estimate by projecting the covariance with MNE
Computing source timecourses with an XFit-like multi-dipole model

Computing source timecourses with an XFit-like multi-dipole model

Computing source timecourses with an XFit-like multi-dipole model
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Compute cross-talk functions for LCMV beamformers

Compute cross-talk functions for LCMV beamformers

Compute cross-talk functions for LCMV beamformers
Plot point-spread functions (PSFs) for a volume

Plot point-spread functions (PSFs) for a volume

Plot point-spread functions (PSFs) for a volume
Compute spatial resolution metrics in source space

Compute spatial resolution metrics in source space

Compute spatial resolution metrics in source space
Compute spatial resolution metrics to compare MEG with EEG+MEG

Compute spatial resolution metrics to compare MEG with EEG+MEG

Compute spatial resolution metrics to compare MEG with EEG+MEG
Computing source space SNR

Computing source space SNR

Computing source space SNR
Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior
Brainstorm raw (median nerve) dataset

Brainstorm raw (median nerve) dataset

Brainstorm raw (median nerve) dataset
Optically pumped magnetometer (OPM) data

Optically pumped magnetometer (OPM) data

Optically pumped magnetometer (OPM) data