mne.preprocessing.nirs.temporal_derivative_distribution_repair

mne.preprocessing.nirs.temporal_derivative_distribution_repair(raw, *, verbose=None)

Apply temporal derivative distribution repair to data.

Applies temporal derivative distribution repair (TDDR) to data [1]. This approach removes baseline shift and spike artifacts without the need for any user-supplied parameters.

Parameters:

rawinstance of Raw

The raw data.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

rawinstance of Raw

Data with TDDR applied.

Notes

TDDR was initially designed to be used on optical density fNIRS data but has been enabled to be applied on hemoglobin concentration fNIRS data as well in MNE. We recommend applying the algorithm to optical density fNIRS data as intended by the original author wherever possible.

There is a shorter alias mne.preprocessing.nirs.tddr that can be used instead of this function (e.g. if line length is an issue).

References

[1]

Frank A Fishburn, Ruth S Ludlum, Chandan J Vaidya, and Andrei V Medvedev. Temporal derivative distribution repair (tddr): a motion correction method for fNIRS. NeuroImage, 184:171–179, 2019. doi:10.1016/j.neuroimage.2018.09.025.

Examples using mne.preprocessing.nirs.temporal_derivative_distribution_repair

Visualise NIRS artifact correction methods

Visualise NIRS artifact correction methods