@inproceedings{ortega-martinez_real-time_2021, author = {Ortega-Martinez, Antonio and L{\"u}hmann, Alexander von and Y{\"u}cel, Meryem A. and Farzam, Parya and Rogers, De'Ja and Boas, David A.}, editor = "Luo, Qingming and Ding, Jun and Fu, Ling", address = "Online Only, United States", title = "Real-time regression and classification of functional near infrared spectroscopy signals acquired during motor tasks", copyright = "All rights reserved", isbn = "978-1-5106-4093-1 978-1-5106-4094-8", url = "https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11629/2578674/Real-time-regression-and-classification-of-functional-near-infrared-spectroscopy/10.1117/12.2578674.full", doi = "10.1117/12.2578674", abstract = "Functional near infrared spectroscopy (fNIRS) is a non-invasive technique for quantifying functional changes in cortical blood volume and oxygenation. Regression techniques are used in cognitive research to separate the neural component from strong physiological and motion artifacts. In this work, we used single stimulus Kalman filter regression to estimate the hemodynamic response function (HRF) produced by subjects performing one of four different tasks (left vs. right finger tapping either overt or covert). We train a linear discriminant analysis (LDA) classifier with a subset of the data and perform cross-validation to estimate mean classification accuracy. The HRF regressed signal displays decreased noise and a modest increase in classification accuracy compared to classification performed on the raw chromophore concentration signal.", urldate = "2022-11-24", booktitle = "Optical {Techniques} in {Neurosurgery}, {Neurophotonics}, and {Optogenetics}", publisher = "SPIE", month = "March", year = "2021", pages = "71" }