mne.AcqParserFIF¶

class mne.AcqParserFIF(info)[source]¶

Parser for Elekta data acquisition settings.

This class parses parameters (e.g. events and averaging categories) that are defined in the Elekta TRIUX/VectorView data acquisition software (DACQ) and stored in info['acq_pars']. It can be used to reaverage raw data according to DACQ settings and modify original averaging settings if necessary.

Parameters

infomne.Info: The mne.Info object with information about the sensors and methods of measurement. This is where the DACQ parameters will be taken from.

See also

mne.io.Raw.acqparser: Access the parser through a Raw attribute.

Notes

Any averaging category (also non-active ones) can be accessed by indexing as acqparserfif['category_name'].

Attributes

categorieslist: Return list of averaging categories ordered by DACQ index.
eventslist: Return events ordered by DACQ index.
rejectdict: Rejection criteria from DACQ that can be used with mne.Epochs. Note that mne does not support all DACQ rejection criteria (e.g. spike, slope).
flatdict: Flatness rejection criteria from DACQ that can be used with mne.Epochs.
acq_dictdict: All DACQ parameters.

Methods

`__getitem__`(item)	Return an averaging category, or list of categories.
`__hash__`(/)	Return hash(self).
`__len__`()	Return number of averaging categories marked active in DACQ.
`get_condition`(raw[, condition, ...])	Get averaging parameters for a condition (averaging category).

__getitem__(item)[source]¶

Return an averaging category, or list of categories.

Parameters

itemstr | list of str: Name of the category (comment field in DACQ).

Returns

condsdict | list of dict

Each dict should have the following keys:

comment: str: The comment field in DACQ.
statebool: Whether the category was marked enabled in DACQ.
indexint: The index of the category in DACQ. Indices start from 1.
eventint: DACQ index of the reference event (trigger event, zero time for the corresponding epochs). Note that the event indices start from 1.
startfloat: Start time of epoch relative to the reference event.
endfloat: End time of epoch relative to the reference event.
reqeventint: Index of the required (conditional) event.
reqwhenint: Whether the required event is required before (1) or after (2) the reference event.
reqwithinfloat: The time range within which the required event must occur, before or after the reference event.
displaybool: Whether the category was displayed online in DACQ.
naveint: Desired number of averages. DACQ stops collecting averages once this number is reached.
subaveint: Whether to compute normal and alternating subaverages, and how many epochs to include. See the Elekta data acquisition manual for details. Currently the class does not offer any facility for computing subaverages, but it can be done manually by the user after collecting the epochs.

__len__()[source]¶

Return number of averaging categories marked active in DACQ.

Returns

n_catint: The number of categories.

property categories¶

Return list of averaging categories ordered by DACQ index.

Only returns categories marked active in DACQ.

property events¶

Return events ordered by DACQ index.

Only returns events that are in use (referred to by a category).

get_condition(raw, condition=None, stim_channel=None, mask=None, uint_cast=None, mask_type='and', delayed_lookup=True)[source]¶

Get averaging parameters for a condition (averaging category).

Output is designed to be used with the Epochs class to extract the corresponding epochs.

Parameters

rawRaw object: An instance of Raw.
conditionNone | str | dict | list of dict: Condition or a list of conditions. Conditions can be strings (DACQ comment field, e.g. ‘Auditory left’) or category dicts (e.g. acqp[‘Auditory left’], where acqp is an instance of AcqParserFIF). If None, get all conditions marked active in DACQ.
stim_channelNone | str | list of str: Name of the stim channel or all the stim channels affected by the trigger. If None, the config variables ‘MNE_STIM_CHANNEL’, ‘MNE_STIM_CHANNEL_1’, ‘MNE_STIM_CHANNEL_2’, etc. are read. If these are not found, it will fall back to ‘STI101’ or ‘STI 014’ if present, then fall back to the first channel of type ‘stim’, if present.
maskint | None: The value of the digital mask to apply to the stim channel values. If None (default), no masking is performed.
uint_castbool: If True (default False), do a cast to uint16 on the channel data. This can be used to fix a bug with STI101 and STI014 in Neuromag acquisition setups that use channel STI016 (channel 16 turns data into e.g. -32768), similar to mne_fix_stim14 --32 in MNE-C.
mask_type‘and’ | ‘not_and’: The type of operation between the mask and the trigger. Choose ‘and’ for MNE-C masking behavior.
delayed_lookupbool: If True, use the ‘delayed lookup’ procedure implemented in Elekta software. When a trigger transition occurs, the lookup of the new trigger value will not happen immediately at the following sample, but with a 1-sample delay. This allows a slight asynchrony between trigger onsets, when they are intended to be synchronous. If you have accurate hardware and want to detect transitions with a resolution of one sample, use delayed_lookup=False.

Returns

conds_datadict or list of dict

Each dict has the following keys:

eventsarray, shape (n_epochs_out, 3): List of zero time points (t0) for the epochs matching the condition. Use as the events parameter to Epochs. Note that these are not (necessarily) actual events.
event_iddict: Name of condition and index compatible with events. Should be passed as the event_id parameter to Epochs.
tminfloat: Epoch starting time relative to t0. Use as the tmin parameter to Epochs.
tmaxfloat: Epoch ending time relative to t0. Use as the tmax parameter to Epochs.

mne.Annotations

mne.concatenate_events