mne.Info

class mne.Info

Information about the recording.

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

The attributes listed below are the possible dictionary entries:

Attributes:

bads : list of str

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

ch_names : list-like of str (read-only)

The names of the channels. This object behaves like a read-only Python list. Behind the scenes it iterates over the channels dictionaries in info[‘chs’]: info[‘ch_names’][x] == info[‘chs’][x][‘ch_name’]

chs : list of dict

A list of channel information structures. See: Frequently Asked Questions for details.

comps : list of dict

CTF software gradient compensation data. See: Frequently Asked Questions for details.

custom_ref_applied : bool

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.

events : list of dict

Event list, usually extracted from the stim channels. See: Frequently Asked Questions for details.

hpi_results : list of dict

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

meas_date : list of int

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

nchan : int

Number of channels.

projs : list of dict

List of SSP operators that operate on the data. See: Frequently Asked Questions for details.

sfreq : float

Sampling frequency in Hertz. See: Frequently Asked Questions for details.

acq_pars : str | None

MEG system acquition parameters.

acq_stim : str | None

MEG system stimulus parameters.

buffer_size_sec : float | None

Buffer size (in seconds) when reading the raw data in chunks.

ctf_head_t : dict | None

The transformation from 4D/CTF head coordinates to Neuromag head coordinates. This is only present in 4D/CTF data. See: Frequently Asked Questions for details.

description : str | None

String description of the recording.

dev_ctf_t : dict | None

The transformation from device coordinates to 4D/CTF head coordinates. This is only present in 4D/CTF data. See: Frequently Asked Questions for details.

dev_head_t : dict | None

The device to head transformation. See: Frequently Asked Questions for details.

dig : list of dict | None

The Polhemus digitization data in head coordinates. See: Frequently Asked Questions for details.

experimentor : str | None

Name of the person that ran the experiment.

file_id : dict | None

The fif ID datastructure of the measurement file. See: Frequently Asked Questions for details.

filename : str | None

The name of the file that provided the raw data.

highpass : float | None

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

hpi_meas : list of dict | None

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

hpi_subsystem : dict | None

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

line_freq : float | None

Frequency of the power line in Hertz.

lowpass : float | None

Lowpass corner frequency in Hertz.

meas_id : dict | None

The ID assigned to this measurement by the acquisition system or during file conversion. See: Frequently Asked Questions for details.

proj_id : int | None

ID number of the project the experiment belongs to.

proj_name : str | None

Name of the project the experiment belongs to.

subject_info : dict | None

Information about the subject.

proc_history : list of dict | None | not present in dict

The SSS info, the CTC correction and the calibaraions from the SSS processing logs inside of a raw file. See: Frequently Asked Questions for details.

Methods

clear(() -> None.  Remove all items from D.)
copy() Copy the instance
fromkeys(...) v defaults to None.
get((k[,d]) -> D[k] if k in D, ...)
has_key((k) -> True if D has a key k, else False)
items(() -> list of D’s (key, value) pairs, ...)
iteritems(() -> an iterator over the (key, ...)
iterkeys(() -> an iterator over the keys of D)
itervalues(...)
keys(() -> list of D’s keys)
normalize_proj() (Re-)Normalize projection vectors after subselection
pop((k[,d]) -> v, ...) If key is not found, d is returned if given, otherwise KeyError is raised
popitem(() -> (k, v), ...) 2-tuple; but raise KeyError if D is empty.
setdefault((k[,d]) -> D.get(k,d), ...)
update(([E, ...) If E present and has a .keys() method, does: for k in E: D[k] = E[k]
values(() -> list of D’s values)
viewitems(...)
viewkeys(...)
viewvalues(...)
__init__()

x.__init__(...) initializes x; see help(type(x)) for signature

clear() → None. Remove all items from D.
copy()

Copy the instance

Returns:

info : instance of Info

The copied info.

fromkeys(S[, v]) → New dict with keys from S and values equal to v.

v defaults to None.

get(k[, d]) → D[k] if k in D, else d. d defaults to None.
has_key(k) → True if D has a key k, else False
items() → list of D's (key, value) pairs, as 2-tuples
iteritems() → an iterator over the (key, value) items of D
iterkeys() → an iterator over the keys of D
itervalues() → an iterator over the values of D
keys() → list of D's keys
normalize_proj()

(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(k[, d]) → v, remove specified key and return the corresponding value.

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

popitem() → (k, v), remove and return some (key, value) pair as a

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

setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D
update([E, ]**F) → None. Update D from dict/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, 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() → list of D's values
viewitems() → a set-like object providing a view on D's items
viewkeys() → a set-like object providing a view on D's keys
viewvalues() → an object providing a view on D's values