{"id":589,"date":"2024-01-12T01:41:18","date_gmt":"2024-01-12T01:41:18","guid":{"rendered":"https:\/\/depts.washington.edu\/chnadmin\/?page_id=589"},"modified":"2025-07-23T01:20:04","modified_gmt":"2025-07-23T01:20:04","slug":"fnirs-system","status":"publish","type":"page","link":"https:\/\/depts.washington.edu\/chnadmin\/fnirs-system\/","title":{"rendered":"fNIRS System"},"content":{"rendered":"

NIRx NIRSport 2 fNIRS system<\/strong><\/p>\n

Thanks to a Student Technology Fee grant to Carly Gray (supervised by Professor Peter Kahn), a NIRx NIRSport 2 wearable fNIRS system, located in a quiet room in CHN (Kincaid Hall), is available to interested researchers across UW. <\/p>\n

Priority is given to undergraduate and graduate student researchers interested in using the system.<\/p>\n

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The system includes 8 sources and 16 detectors, 16 short separation channels, a mobility pack, the NIRx WINGS system for measuring peripheral physiological signals (e.g., heart rate, body temperature, SpO2, etc.), and data acquisition software (Aurora fNIRS recording software). Nine cap sizes are available between sizes 40-60cm.<\/p>\n

It can record at sampling between 70-240 Hz. Data and event trigger signals may be transmitted via WiFi (for ambulatory recordings) or USB. Data may also be recorded directly to the system.<\/p>\n

Investigators are responsible for obtaining IRB approval. <\/p>\n

To gain access to this resource please email chnadmin@uw.edu<\/a> and ionefine@uw.edu<\/a>. <\/p>\n

Once approved for using the system you can book time on the calendar here<\/a>.<\/p>\n<\/p>\n

USEFUL RESOURCES (Thanks to Carly Gray)<\/strong><\/h2>\n

Articles<\/u><\/strong><\/p>\n

General reviews (especially focused on use of fNIRS in real-world, ecologically valid settings):<\/strong><\/p>\n

Pinti, P., Tachtsidis, I., Burgess, P. W., & Hamilton, A. F. D. C. (2023). Non-invasive optical imaging of brain function with fNIRS: Current status and way forward. In Reference Module in Neuroscience and Biobehavioral Psychology<\/em> (p. B9780128204801000280). Elsevier. https:\/\/doi.org\/10.1016\/B978-0-12-820480-1.00028-0<\/a><\/p>\n

Pinti, P., Aichelburg, C., Lind, F., Power, S., Swingler, E., Merla, A., Hamilton, A., Gilbert, S., Burgess, P., & Tachtsidis, I. (2015). Using fiberless, wearable fNIRS to monitor brain activity in real-world cognitive tasks. JoVE (Journal of Visualized Experiments)<\/em>, 106<\/em>, e53336. https:\/\/doi.org\/10.3791\/53336<\/a><\/p>\n

Y\u00fccel, M. A., L\u00fchmann, A. v., Scholkmann, F., Gervain, J., Dan, I., Ayaz, H., Boas, D., Cooper, R. J., Culver, J., Elwell, C. E., Eggebrecht, A., Franceschini, M. A., Grova, C., Homae, F., Lesage, F., Obrig, H., Tachtsidis, I., Tak, S., Tong, Y., \u2026 Wolf, M. (2021). Best practices for fNIRS publications. Neurophotonics<\/em>, 8<\/em>(1), 012101. https:\/\/doi.org\/10.1117\/1.NPh.8.1.012101<\/a><\/p>\n

Analytic approaches and issues (especially focused on motion artifacts):<\/strong><\/p>\n

Barker, J. W., Aarabi, A., & Huppert, T. J. (2013). Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS. Biomedical Optics Express<\/em>, 4<\/em>(8), 1366\u20131379. https:\/\/doi.org\/10.1364\/BOE.4.001366<\/a><\/p>\n

Brigadoi, S., Ceccherini, L., Cutini, S., Scarpa, F., Scatturin, P., Selb, J., Gagnon, L., Boas, D. A., & Cooper, R. J. (2014). Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data. NeuroImage<\/em>, 85<\/em>, 181\u2013191. https:\/\/doi.org\/10.1016\/j.neuroimage.2013.04.082<\/a><\/p>\n

Huppert, T. J. (2016). Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy. Neurophotonics<\/em>, 3<\/em>(1), 010401. https:\/\/doi.org\/10.1117\/1.NPh.3.1.010401<\/a><\/p>\n

Pinti, P., Scholkmann, F., Hamilton, A., Burgess, P., & Tachtsidis, I. (2019). Current status and issues regarding pre-processing of fNIRS neuroimaging data: An investigation of diverse signal filtering methods within a general linear model framework. Frontiers in Human Neuroscience<\/em>, 12<\/em>. https:\/\/www.frontiersin.org\/articles\/10.3389\/fnhum.2018.00505<\/a><\/p>\n

Tachtsidis, I., & Scholkmann, F. (2016). False positives and false negatives in functional near-infrared spectroscopy: Issues, challenges, and the way forward. Neurophotonics<\/em>, 3<\/em>(3), 031405. https:\/\/doi.org\/10.1117\/1.NPh.3.3.031405<\/a><\/p>\n

Tak, S., & Ye, J. C. (2014). Statistical analysis of fNIRS data: A comprehensive review. NeuroImage<\/em>, 85<\/em>, 72\u201391. https:\/\/doi.org\/10.1016\/j.neuroimage.2013.06.016<\/a><\/p>\n

von L\u00fchmann, A., Li, X., M\u00fcller, K.-R., Boas, D. A., & Y\u00fccel, M. A. (2020). Improved physiological noise regression in fNIRS: A multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis. NeuroImage<\/em>, 208<\/em>, 116472. https:\/\/doi.org\/10.1016\/j.neuroimage.2019.116472<\/a><\/p>\n

von L\u00fchmann, A., Ortega-Martinez, A., Boas, D. A., & Y\u00fccel, M. A. (2020). Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective. Frontiers in Human Neuroscience<\/em>, 14<\/em>. https:\/\/www.frontiersin.org\/articles\/10.3389\/fnhum.2020.00030<\/a><\/p>\n

Inclusivity of darker skin tones and hair types in fNIRS research:<\/strong><\/p>\n

Kwasa, J., Peterson, H. M., Karrobi, K., Jones, L., Parker, T., Nickerson, N., & Wood, S. (2023). Demographic reporting and phenotypic exclusion in fNIRS. Frontiers in Neuroscience<\/em>, 17<\/em>. https:\/\/www.frontiersin.org\/articles\/10.3389\/fnins.2023.1086208<\/a><\/p>\n

Videos I found useful:<\/u><\/strong><\/p>\n

NIRx’s YouTube channel<\/a> is really useful in general!<\/p>\n