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.

Note

This class should not be instantiated directly via mne.Info(...). Instead, use mne.create_info() to create measurement information from scratch.

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).

Parameters:

*argslist: Arguments.
**kwargsdict: Keyword arguments.

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 an average projector) 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 v0.19.
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 v0.19.
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 v0.24.
utc_offsetstr: “UTC offset of related meas_date (sHH:MM).

New in v0.19.
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__`(key, /)	Return self[key].
`__iter__`(/)	Implement iter(self).
`__len__`(/)	Return len(self).
`anonymize`([daysback, keep_his, verbose])	Anonymize measurement information in place.
`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.
`plot_sensors`([kind, ch_type, title, ...])	Plot sensor positions.
`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.
`rename_channels`(mapping[, allow_duplicates, ...])	Rename channels.
`save`(fname)	Write measurement info in fif file.
`set_channel_types`(mapping, *[, ...])	Specify the sensor types of channels.
`set_meas_date`(meas_date)	Set the measurement start date.
`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`()

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 information. The first three elements [:3] always store the nominal channel position. The remaining 9 elements store different information based on the channel type:

MEG
Remaining 9 elements [3:], contain the EX, EY, and EZ normal triplets (columns) of the coil rotation/orientation matrix.

EEG
Elements [3:6] contain the reference channel position.

Eyetrack
Element [3] contains information about which eye was tracked (-1 for left, 1 for right), and element [4] contains information about the the axis of coordinate data (-1 for x-coordinate data, 1 for y-coordinate data).

Dipole
Elements [3:6] contain dipole orientation information.

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_datedatetime.datetime
The helium level meas date.

Changed in version 1.8: This is stored as a datetime object instead of a tuple of seconds/microseconds.
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 Transform
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.

birthdaydatetime.date
The subject birthday.

Changed in version 1.8: This is stored as a date object instead of a tuple of seconds/microseconds.

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.

__contains__(key, /)#: True if the dictionary has the specified key, else False.

__getitem__(key, /)#: Return self[key].

__iter__(/)#: Implement iter(self).

__len__(/)#: Return len(self).

anonymize(daysback=None, keep_his=False, verbose=None)[source]#

Anonymize measurement information in place.

Parameters:

daysbackint | None: Number of days to subtract from all dates. If None (default), the acquisition date, info['meas_date'], will be set to January 1ˢᵗ, 2000. This parameter is ignored if info['meas_date'] is None (i.e., no acquisition date has been set).
keep_hisbool: If True, his_id of subject_info will not be overwritten. Defaults to False.

Warning

This could mean that info is not fully anonymized. Use with caution.
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 modified instance.

Notes

Removes potentially identifying information if it exists in info. Specifically for each of the following we use:

meas_date, file_id, meas_id
A default value, or as specified by daysback.
subject_info
Default values, except for ‘birthday’ which is adjusted to maintain the subject age.
experimenter, proj_name, description
Default strings.
utc_offset
None.
proj_id
Zeros.
proc_history
Dates use the meas_date logic, and experimenter a default string.
helium_info, device_info
Dates use the meas_date logic, meta info uses defaults.

If info['meas_date'] is None, it will remain None during processing the above fields.

Operates in place.

New in v0.13.0.

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

plot_sensors(kind='topomap', ch_type=None, title=None, show_names=False, ch_groups=None, to_sphere=True, axes=None, block=False, show=True, sphere=None, *, verbose=None)[source]#

Plot sensor positions.

Parameters:

kindstr: Whether to plot the sensors as 3d, topomap or as an interactive sensor selection dialog. Available options ‘topomap’, ‘3d’, ‘select’. If ‘select’, a set of channels can be selected interactively by using lasso selector or clicking while holding control key. The selected channels are returned along with the figure instance. Defaults to ‘topomap’.
ch_typeNone | str: The channel type to plot. Available options 'mag', 'grad', 'eeg', 'seeg', 'dbs', 'ecog', 'all'. If 'all', all the available mag, grad, eeg, seeg, dbs, and ecog channels are plotted. If None (default), then channels are chosen in the order given above.
titlestr | None: Title for the figure. If None (default), equals to 'Sensor positions (%s)' % ch_type.
show_namesbool | array of str: Whether to display all channel names. If an array, only the channel names in the array are shown. Defaults to False.
ch_groups‘position’ | array of shape (n_ch_groups, n_picks) | None: Channel groups for coloring the sensors. If None (default), default coloring scheme is used. If ‘position’, the sensors are divided into 8 regions. See order kwarg of mne.viz.plot_raw(). If array, the channels are divided by picks given in the array.

New in v0.13.0.
to_spherebool: Whether to project the 3d locations to a sphere. When False, the sensor array appears similar as to looking downwards straight above the subject’s head. Has no effect when kind=’3d’. Defaults to True.

New in v0.14.0.
axesinstance of Axes | instance of Axes3D | None: Axes to draw the sensors to. If kind='3d', axes must be an instance of Axes3D. If None (default), a new axes will be created.

New in v0.13.0.
blockbool: Whether to halt program execution until the figure is closed. Defaults to False.

New in v0.13.0.
showbool: Show figure if True. Defaults to True.
spherefloat | array_like | instance of ConductorModel | None | ‘auto’ | ‘eeglab’: The sphere parameters to use for the head outline. Can be array-like of shape (4,) to give the X/Y/Z origin and radius in meters, or a single float to give just the radius (origin assumed 0, 0, 0). Can also be an instance of a spherical ConductorModel to use the origin and radius from that object. If 'auto' the sphere is fit to digitization points. If 'eeglab' the head circle is defined by EEG electrodes 'Fpz', 'Oz', 'T7', and 'T8' (if 'Fpz' is not present, it will be approximated from the coordinates of 'Oz'). None (the default) is equivalent to 'auto' when enough extra digitization points are available, and (0, 0, 0, 0.095) otherwise.

New in v0.20.

Changed in version 1.1: Added 'eeglab' option.
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:

figinstance of Figure: Figure containing the sensor topography.
selectionlist: A list of selected channels. Only returned if kind=='select'.

See also

mne.viz.plot_layout

Notes

This function plots the sensor locations from the info structure using matplotlib. For drawing the sensors using PyVista see mne.viz.plot_alignment().

New in v0.12.0.

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.

rename_channels(mapping, allow_duplicates=False, *, verbose=None)[source]#

Rename channels.

Parameters:

mappingdict | callable(): A dictionary mapping the old channel to a new channel name e.g. {'EEG061' : 'EEG161'}. Can also be a callable function that takes and returns a string.

Changed in version 0.10.0: Support for a callable function.
allow_duplicatesbool: If True (default False), allow duplicates, which will automatically be renamed with -N at the end.

New in v0.22.0.
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).

Changed in version 0.20: Return the instance.

Notes

New in v0.9.0.

save(fname)[source]#

Write measurement info in fif file.

Parameters:

fnamepath-like: The name of the file. Should end by '-info.fif'.

set_channel_types(mapping, *, on_unit_change='warn', verbose=None)[source]#

Specify the sensor types of channels.

Parameters:

mappingdict: A dictionary mapping channel names to sensor types, e.g., {'EEG061': 'eog'}.
on_unit_change'raise' | 'warn' | 'ignore': What to do if the measurement unit of a channel is changed automatically to match the new sensor type.

New in v1.4.
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).

Changed in version 0.20: Return the instance.

Notes

The following sensor types are accepted:

bio, chpi, csd, dbs, dipole, ecg, ecog, eeg, emg, eog, exci, eyegaze, fnirs_cw_amplitude, fnirs_fd_ac_amplitude, fnirs_fd_phase, fnirs_od, gof, gsr, hbo, hbr, ias, misc, pupil, ref_meg, resp, seeg, stim, syst, temperature.

When working with eye-tracking data, see mne.preprocessing.eyetracking.set_channel_types_eyetrack().

New in v0.9.0.

set_meas_date(meas_date)[source]#

Set the measurement start date.

Parameters:

meas_datedatetime | float | tuple | None: The new measurement date. If datetime object, it must be timezone-aware and in UTC. A tuple of (seconds, microseconds) or float (alias for (meas_date, 0)) can also be passed and a datetime object will be automatically created. If None, will remove the time reference.

Returns:

instinstance of Raw | Epochs | Evoked: The modified raw instance. Operates in place.

See also

mne.io.Raw.anonymize

Notes

If you want to remove all time references in the file, call mne.io.anonymize_info(inst.info) after calling inst.set_meas_date(None).

New in v0.20.

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 v0.20.
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 v0.23.
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 v0.20.1.
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.

See also

mne.channels.make_standard_montage
mne.channels.make_dig_montage
mne.channels.read_custom_montage

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.

Warning

Applying a montage will only set locations of channels that exist at the time it is applied. This means when re-referencing make sure to apply the montage only after calling mne.add_reference_channels()

Examples using set_montage:

Creating MNE-Python data structures from scratch

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`#

Overview of MEG/EEG analysis with MNE-Python

Modifying data in-place

Parsing events from raw data

The Info data structure

Working with sensor locations

Getting started with mne.Report

Importing data from fNIRS devices

Working with CTF data: the Brainstorm auditory dataset

The Raw data structure: continuous data

Working with events

Annotating continuous data

Built-in plotting methods for Raw objects

Overview of artifact detection

Handling bad channels

Rejecting bad data spans and breaks

Filtering and resampling data

Repairing artifacts with regression

Background on projectors and projections

Repairing artifacts with SSP

Setting the EEG reference

Extracting and visualizing subject head movement

Signal-space separation (SSS) and Maxwell filtering

Preprocessing functional near-infrared spectroscopy (fNIRS) data

Preprocessing optically pumped magnetometer (OPM) MEG data

Visualizing epoched data

Auto-generating Epochs metadata

Visualizing Evoked data

EEG analysis - Event-Related Potentials (ERPs)

Frequency and time-frequency sensor analysis

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

Source alignment and coordinate frames

Using an automated approach to coregistration

Head model and forward computation

EEG forward operator with a template MRI

How MNE uses FreeSurfer’s outputs

How MNE uses FreeSurfer's outputs

Computing a covariance matrix

Source localization with equivalent current dipole (ECD) fit

Source localization with MNE, dSPM, sLORETA, and eLORETA

The role of dipole orientations in distributed source localization

Computing various MNE solutions

Source reconstruction using an LCMV beamformer

EEG source localization given electrode locations on an MRI

Brainstorm Elekta phantom dataset tutorial

4D Neuroimaging/BTi phantom dataset tutorial

KIT phantom dataset tutorial

Visualising statistical significance thresholds on EEG data

Non-parametric 1 sample 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

Spatiotemporal permutation F-test on full sensor data

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

Repeated measures ANOVA on source data with spatio-temporal clustering

Decoding (MVPA)

Working with sEEG data

Working with ECoG data

Creating MNE-Python data structures from scratch

Corrupt known signal with point spread

DICS for power mapping

Make figures more publication ready

Getting impedances from raw files

How to use data in neural ensemble (NEO) format

Reading/Writing a noise covariance matrix

Reading XDF EEG data

Compare simulated and estimated source activity

Generate simulated evoked data

Generate simulated raw data

Simulate raw data using subject anatomy

Generate simulated source data

Using contralateral referencing for EEG

Cortical Signal Suppression (CSS) for removal of cortical signals

Define target events based on time lag, plot evoked response

Identify EEG Electrodes Bridged by too much Gel

Show EOG artifact timing

Automated epochs metadata generation with variable time windows

Find MEG reference channel artifacts

Visualise NIRS artifact correction methods

Maxwell filter data with movement compensation

Annotate movement artifacts and reestimate dev_head_t

XDAWN Denoising

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

Plotting with mne.viz.Brain

Visualize channel over epochs as an image

Plotting EEG sensors on the scalp

Plotting topographic arrowmaps of evoked data

Whitening evoked data with a noise covariance

Plotting sensor layouts of MEG systems

Plot the MNE brain and helmet

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

Plot custom topographies for MEG sensors

Compute a cross-spectral density (CSD) matrix

Compute Power Spectral Density of inverse solution from single epochs

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) of VectorView and OPM data

Compute induced power in the source space with dSPM

Temporal whitening with AR model

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

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

FDR correction on T-test on sensor data

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

Permutation T-test on sensor data

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

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

Decoding source space data

Continuous Target Decoding with SPoC

Decoding sensor space data with generalization across time and conditions

Analysis of evoked response using ICA and PCA reduction techniques

XDAWN Decoding From EEG data

Compute effect-matched-spatial filtering (EMS)

Linear classifier on sensor data with plot patterns and filters

Receptive Field Estimation and Prediction

Compute spatial filters with Spatio-Spectral Decomposition (SSD)

Use source space morphing

Compute MNE-dSPM inverse solution on single epochs

Source localization with a custom inverse solver

Compute source level time-frequency timecourses using a DICS beamformer

Compute source power using DICS beamformer

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

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

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

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

Compute source power estimate by projecting the covariance with MNE

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

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

Compute cross-talk functions for LCMV beamformers

Plot point-spread functions (PSFs) for a volume

Compute spatial resolution metrics in source space

Compute spatial resolution metrics to compare MEG with EEG+MEG

Computing source space SNR

Compute MxNE with time-frequency sparse prior

Brainstorm raw (median nerve) dataset

Kernel OPM phantom data

Optically pumped magnetometer (OPM) data

From raw data to dSPM on SPM Faces dataset

mne.Info#

Examples using mne.Info#

Examples using `mne.Info`#