Linear regression models and k-means clustering for statistical analysis of fNIRS data |
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| Authors: | Viola Bonomini Lucia Zucchelli Rebecca Re Francesca Ieva Lorenzo Spinelli Davide Contini Anna Paganoni Alessandro Torricelli |
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| Affiliation: | 1.MOX - Department of Mathematics, Politecnico di Milano, Milan, Italy;2.Dipartimento di Fisica, Politecnico di Milano, Milan, Italy;3.Department of Mathematics “Federigo Enriques”, Università degli Studi di Milano, Milan, Italy;4.Istituto di Fotonica e Nanotecnologie, CNR,Milan, Italy;5.first two authors contributed equally to this work |
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| Abstract: | ![]()
We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets.OCIS codes: (000.5490) Probability theory, stochastic processes, and statistics; (170.2655) Functional monitoring and imaging; (170.6920) Time-resolved imaging; (170.1470) Blood or tissue constituent monitoring |
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