@article{ortega-martinez_multivariate_2022,
    author = {Ortega-Martinez, Antonio and Von L{\"u}hmann, Alexander and Farzam, Parya and Rogers, De’Ja and Mugler, Emily M. and Boas, David A. and Y{\"u}cel, Meryem A.},
    title = "Multivariate {Kalman} filter regression of confounding physiological signals for real-time classification of {fNIRS} data",
    volume = "9",
    copyright = "All rights reserved",
    issn = "2329-423X",
    url = "https://www.spiedigitallibrary.org/journals/neurophotonics/volume-9/issue-02/025003/Multivariate-Kalman-filter-regression-of-confounding-physiological-signals-for-real/10.1117/1.NPh.9.2.025003.full",
    doi = "10.1117/1.NPh.9.2.025003",
    abstract = "Significance: Functional near-infrared spectroscopy (fNIRS) is a noninvasive technique for measuring hemodynamic changes in the human cortex related to neural function. Due to its potential for miniaturization and relatively low cost, fNIRS has been proposed for applications, such as brain–computer interfaces (BCIs). The relatively large magnitude of the signals produced by the extracerebral physiology compared with the ones produced by evoked neural activity makes real-time fNIRS signal interpretation challenging. Regression techniques incorporating physiologically relevant auxiliary signals such as short separation channels are typically used to separate the cerebral hemodynamic response from the confounding components in the signal. However, the coupling of the extra-cerebral signals is often noninstantaneous, and it is necessary to find the proper delay to optimize nuisance removal.",
    language = "en",
    number = "02",
    urldate = "2022-11-21",
    journal = "Neurophotonics",
    month = "June",
    year = "2022",
    keywords = "Featured"
}