mne.io.Info

class mne.io.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 of str

The names of the channels.

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. See: subject_info for details

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)
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
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