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

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

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