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Temporal Derivative Distribution Repair (TDDR)

TDDR demonstration

About

The code within this repository is the reference implementation for the TDDR algorithm as described in:

Fishburn F.A., Ludlum R.S., Vaidya C.J., & Medvedev A.V. (2019). Temporal Derivative Distribution Repair (TDDR): A motion correction method for fNIRS. NeuroImage, 184, 171-179. doi: 10.1016/j.neuroimage.2018.09.025

Usage

Matlab:

signals_corrected = TDDR(signals, sample_rate);

Python:

from TDDR import TDDR
signals_corrected = TDDR(signals, sample_rate);

Inputs

signals: A [sample x channel] matrix of uncorrected optical density data

sample_rate: A scalar reflecting the rate of acquisition in Hz

Outputs

signals_corrected: A [sample x channel] matrix of corrected optical density data

Toolboxes

NIRS Brain AnalyzIR

The TDDR algorithm is implemented in the nirs.modules.TDDR module. Typical usage looks like this:

% Construct preprocessing job with TDDR motion correction
job = nirs.modules.OpticalDensity();
job = nirs.modules.TDDR(job);
job = nirs.modules.BeerLambertLaw(job);

% Run job on raw data
hb = job.run(raw);

Homer2

While Homer2 does not yet contain the TDDR method, a Homer2-compatible script is available in this repository at toolboxes/Homer2/hmrMotionCorrectTDDR.m. Usage is similar to other motion correction scripts shipped by Homer2.