Real-time regression and classification of functional near infrared spectroscopy signals acquired during motor tasks

Antonio Ortega-Martinez; Alexander von Lühmann; Meryem A. Yücel; Parya Farzam; De'Ja Rogers; David A. Boas

doi:10.1117/12.2578674

5 March 2021 Real-time regression and classification of functional near infrared spectroscopy signals acquired during motor tasks

Antonio Ortega-Martinez, Alexander von Lühmann, Meryem A. Yücel, Parya Farzam, De'Ja Rogers, David A. Boas

Author Affiliations +

Proceedings Volume 11629, Optical Techniques in Neurosurgery, Neurophotonics, and Optogenetics; 116292M (2021) https://doi.org/10.1117/12.2578674
Event: SPIE BiOS, 2021, Online Only

Conference Poster

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.

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Citation Download Citation

Antonio Ortega-Martinez, Alexander von Lühmann, Meryem A. Yücel, Parya Farzam, De'Ja Rogers, and David A. Boas "Real-time regression and classification of functional near infrared spectroscopy signals acquired during motor tasks", Proc. SPIE 11629, Optical Techniques in Neurosurgery, Neurophotonics, and Optogenetics, 116292M (5 March 2021); https://doi.org/10.1117/12.2578674

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