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

__contains__((k) -> True if D has a key k, ...)
__getitem__ x.__getitem__(y) <==> x[y]
__iter__() <==> iter(x)
__len__() <==> len(x)
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(...)
__contains__(k) → True if D has a key k, else False
__getitem__()

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

__iter__() <==> iter(x)
__len__() <==> len(x)
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