mne.preprocessing.nirs.temporal_derivative_distribution_repair

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

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, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

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

mne.preprocessing.nirs.scalp_coupling_index mne.add_reference_channels