In order to objectively evaluate the power of methods which aim to remove confounding resting state physiological factors such as blood flow, low frequency oscillation, and breathing from fNIRS data, it is useful to have a dataset with known ground truth. In order to generate an fNIRS signal with confounding signals but also a known hemodynamic response function (HRF), real resting state fNIRS data is recorded, and a modeled HRF is added. The data also features auxilliary measurements such as accelerometer and photoplethysmography signals.

Year: 2020
Format: Snirf
Stimuli type: Resting state with synthetic HRF

DATASET 1 | DATASET 2
Aux Meas: PPG, RESP, BP, Accelerometer | Accelerometer
Num of Subj : 14 | 14
Num of Trial: 15 | 38
Age: 21 ± 2 | 32 ± 19
Sex (M/F): 11/3 | 7/5
Optode Coverage: Occipital | Fronto-parietal
LS Channel: 26 | 48
SS Channel: 2 | 8
Duration (min): 5 | 10
Sample Rate (Hz): 50 | 50
Wavelengths: 690/830 | 690/830
Acq. System: TechEn CW4 | TechEnCW5