# mne.Info¶

class mne.Info[source]

Measurement information.

This data structure behaves like a dictionary. It contains all metadata that is available for a recording.

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

The only entries that should be manually changed by the user are info['bads'] and info['description']. All other entries should be considered read-only, or should be modified by functions or methods.

Parameters
acq_pars

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

acq_stim

MEG system stimulus parameters.

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

ch_names

The names of the channels.

chs

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

comps

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

custom_ref_appliedbool

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.

description

String description of the recording.

dev_ctf_t

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

dig

events

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.

experimenter

Name of the person that ran the experiment.

file_id

highpassfloat

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

hpi_meas

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

hpi_results

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

hpi_subsystem

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

line_freq

Frequency of the power line in Hertz.

gantry_angle

Tilt angle of the gantry in degrees.

lowpassfloat

Lowpass corner frequency in Hertz.

meas_date

The first element of this list is a UNIX timestamp (seconds since 1970-01-01 00:00:00) denoting the date and time at which the measurement was taken. The second element is the additional number of microseconds.

meas_id

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

nchanint

Number of channels.

proc_history

The MaxFilter processing history. See Notes for details.

proj_id

ID number of the project the experiment belongs to.

proj_name

Name of the project the experiment belongs to.

projs

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

sfreqfloat

Sampling frequency in Hertz.

subject_info

Information about the subject. See Notes for details.

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

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.

• dig list:

• 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.

• 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

• 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).

• 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 acquisiton.

• 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).

Methods

 __contains__(self, key, /) True if the dictionary has the specified key, else False. x.__getitem__(y) <==> x[y] __iter__(self, /) Implement iter(self). __len__(self, /) Return len(self). copy(self) Copy the instance. fromkeys(iterable[, value]) Create a new dictionary with keys from iterable and values set to value. get(self, key[, default]) Return the value for key if key is in the dictionary, else default. normalize_proj(self) (Re-)Normalize projection vectors after subselection. If key is not found, d is returned if given, otherwise KeyError is raised 2-tuple; but raise KeyError if D is empty. setdefault(self, key[, default]) Insert key with a value of default if key is not in the dictionary. If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
__contains__(self, key, /)

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

__getitem__()

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

__iter__(self, /)

Implement iter(self).

__len__(self, /)

Return len(self).

clear()
copy(self)[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(self, key, default=None, /)

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

items()
keys()
normalize_proj(self)[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.

pop()

If key is not found, d is returned if given, otherwise KeyError is raised

popitem()

2-tuple; but raise KeyError if D is empty.

setdefault(self, 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()

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values()