fNIRS is a non-invasive, portable and low-cost optical neuroimaging technology with considerable spatial and temporal resolution. Due to the late emergence of fNIRS technology compared to EEG, the research results using this technology are not at the same level as those using EEG. A search on Google datasets shows that there is no systematic public dataset for fNIRS.
The following public datasets are compiled from Google Scholar literature.
1. Defenderfer 2019; fNIRS data files for event-related vocoding/background noise study
A total of 38 fNIRS datasets shared by the Defenderfer team from the University of Tennessee. The dataset contains functional near-infrared data obtained when subjects completed noise coding speech tasks and background white noise speech perception tasks.
Dataset access link: http://dx.doi.org/10.17632/4cjgvyg5p2.1
Dataset citation: Defenderfer, Jessica; Buss, Aaron (2019), “Defenderfer 2019; fNIRS data files for event-related vocoding/background noise study”, Mendeley Data, V1, doi: 10.17632/4cjgvyg5p2.1
2. Space Technology Mission Directorate 2020; Interference Tolerant Functional Near Infrared Spectrometer (fNIRS) for Cognitive State Monitoring
NASA's Space Technology Mission Directorate released a dataset in 2020 that uses fNIRS technology to monitor real-time cognitive states of astronauts during space missions.The experiments mainly focus on warning and intervention when crew members' brain function declines while performing tasks related to space operations safety.This dataset is publicly available but NASA does not provide direct links for download.You can apply via email as detailed on their website.
Dataset webpage link:https://data.nasa.gov/dataset/Interference-Tolerant-Functional-Near-Infrared-Spe/rhtq-aacw
Dataset project link: https://techport.nasa.gov/view/93879
3. Shixian liu 2020, fNIRS DATA
A set of frontal lobe fNIRS data obtained when stroke patients and normal subjects performed hand movements (left and right hands). The dataset contains a total of 9 pairs of data from 18 subjects (each pair includes one healthy person's left and right hand movement data and one patient's left and right hand movement data). Detailed information can be found in the "data" folder of the dataset.
Dataset access link: http://dx.doi.org/10.17632/jhv73gj2d2.1
Dataset citation: liu, shixian (2020), “fNIRS DATA”, Mendeley Data, V1, doi: 10.17632/jhv73gj2d2.1
4. Kevin 2014, Motion Artifact Contaminated fNIRS and EEG Data
This dataset is shared on PhysioBank by Kevin Sweeney and his colleagues at the National University of Ireland.It includes two types of data:fNIRS and EEG.The purpose of creating this dataset was to validate a new artifact removal method.Currently there are six literature references citing this dataset according to Google Scholar.
Dataset access link:https://doi.org/10.13026/C2988P
Dataset citation:Sweeney KT, Ayaz H, Ward TE, Izzetoglu M, McLoone SF, Onaral B.A Methodology for Validating Artifact Removal Techniques for Physiological Signals.IEEE Trans Info Tech Biomed16(5):918-926;2012(Sept).
5. Liping Qi 2019,fNIRS data
A fNIRS dataset shared by a scholar from Dalian University of Technology in China on an open platform. The dataset contains four blocks of data, including baseline stroop tasks, rest state, tai chi and brisk walking.
Dataset access link: http://dx.doi.org/10.17632/wrmdw82nzr.1
Dataset citation: Qi, Liping (2019), “fNIRS data”, Mendeley Data, V1, doi: 10.17632/wrmdw82nzr.1
6. Benitez 2020, The influence of extra-cerebral vasculature on the efficacy of the short-separation regression approach applied to fNIRS data analysis.
A dataset of fNIRS was released by Maastricht University to verify the impact of SSR on the quality and sensitivity of fNIRS data. Previous studies have solved the problem of low signal-to-noise ratio in fNIRS signals by using additional information recorded from outside areas of the brain through short-distance channels (SDC) to correct them. This method, called Short Separation Regression (SSR), can improve both signal quality and sensitivity for detecting task-related brain activation with fNIRS. However, it is currently unclear whether the effectiveness of SSR depends on factors such as vascular proximity/density near channels.
The research team combined physiological anatomy, function, and vascular imaging magnetic resonance imaging data with continuous wave fNIRS data to quantify the impact of SSR on signal quality and sensitivity for detecting task-related brain activation while investigating how vascular proximity/density affects its effectiveness.
Dataset address: https://doi.org/10.34894/GALD5F Note: Authorization is required to access and download this dataset.
Dataset citation: Benitez-Andonegui, A.; Turšič, A.; Dumitrescu, S.; Ivanov, D; Goebel, R.; Lührs,M; Sorger,B., 2020,"The influenceofextra-cerebralvasculatureontheefficacyoftheshort-separationregressionapproachappliedtofNIR Sdataanalysis",https://doi.org/10.34894/GALD5F,DataverseNL,V1 Currently there are 55 literature citations found in Google Scholar referencing this dataset.
7. San Juan 2017, Replication Data for: fNIRS RSFC in Tinnitus
This fNIRS dataset was shared by the San Juan team on Harvard Dataverse. The research team used fNIRS technology to study changes in functional connectivity of human auditory and non-auditory brain regions during resting state before and after auditory stimulation under normal hearing, bilateral subjective tinnitus, and control conditions. The related research results were published in PLoS ONE in 2016.
Dataset publication: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0179150 Dataset access link: https://doi.org/10.7910/DVN/ZNZZBV
Dataset citation: San Juan, Juan, 2017, "Replication Data for: fNIRS RSFC in Tinnitus", https://doi.org/10.7910/DVN/ZNZZBV, Harvard Dataverse, V1
8. Peter Mukli 2021, Mental workload during n-back task captured by TransCranial Doppler (TCD) sonography and functional Near-Infrared Spectroscopy (fNIRS) monitoring
A study investigating cognitive load during n-Back tasks using transcranial Doppler (TCD) and fNIRS technologies with a total of 14 healthy young people's fNIRS data.
Publication and dataset access link:https://doi.org/10.13026/zfb2-1g43
Dataset citation:Mukli,P.,Yabluchanskiy,A.,&Csipo,T.(2021).Mental workload during n-back task captured by TransCranial Doppler(TCD)sonographyandfunctionalNear-InfraredSpectroscopy(fNIR S)monitoring(version1 .0).PhysioNet.https://doi.org/10.13026/zfb2-1g43.
9. Sujin Bak 2019, Data from: Open Access fNIRS Dataset for Classification of Unilateral Finger- and Foot-Tapping
BCI data of 30 cases performing unilateral finger and foot tapping tasks in mat format.
Dataset access link:https://doi.org/10.6084/m9.figshare.9783755.v2
Dataset citation:Bak,Sujin;Park,Jinwoo;Shin,Jaeyoung;Jeong,Jichai(2019):OpenAccessfNIRSDatasetforClassificationofUnilateralFinger-andFoot-Tapping.figshare.Dataset.https://doi.org/10.6084/m9.figshare.9783755.v2
10. peng shang 2020, normal and stroke raw fNIRS data.
Raw fNIRS data of healthy individuals and stroke patients collected using a portable NIRSIT device in .db format. There are 10 sets of data from normal individuals and 18 sets of data from stroke patients.
Original dataset link: https://doi.org/10.17632/63rszpyfns.2
Pre-processed dataset link: https://doi.org/10.17632/6mbzffznr6.1
Dataset citation: Shang, Peng; Liu, Shixian (2020), "normal and stroke", Mendeley Data, V2, doi: 10.17632/63rszpyfns.2
11. Clinical, neuropsychological and NIRS neurophysiological data in children with ADHD and typically developing peers
Clinical, neuropsychological and NIRS neurophysiological data of 43 children with ADHD compared to typically developing peers hosted on Zenodo (a multidisciplinary research database). Access permission can be obtained through application.
Detailed information about the dataset: https://doi.org/10.5281/zenodo.4537221
Dataset citation: Maddalena Mauri, Silvia Grazioli, Alessandro Crippa, Andrea Bacchetta,Uberto Pozzoli,Silvana Bertella,...Maria Nobile.(2021). Clinical ,neuropsychologicaland NIRSn europhysiologicaldatainchildrenwithADHDandtypicallydevelopingpeers[Datas et].JournalofAffectiveDisorders.Zenodo.<http://doi.org /10 .5281 /zenodo .4537221 >
12. Hudak2017,functionalnear-infraredspectroscopyNeurofeedbackinADHD
30 sessions of expected Matlab fNIRS neurofeedback training data from 19 adult ADHD patients, all stored in .txt format with detailed instructions.
Dataset link: https://doi.org/10.17026/dans-xrk-hnhb
Dataset citation format: Hudak, J.P. (University of Tübingen) (2017): Functional near-infrared spectroscopy Neurofeedback in ADHD. DANS.https://doi.org/10.17026/dans-xrk-hnhb
13. Günther Bauernfeind 2014,fNIRS – BCI mental arithmetic(003-2014)
BCI classification data of eight adults performing mental arithmetic tasks. Detailed information can be found in the PDF documentation.
Dataset link: http://bnci-horizon-2020.eu/database/data-sets
Note: This link contains multiple BCI - EEG datasets and the third dataset is an fNIRS dataset.