**Definition:** General linear model is a linear regression model that allows for analyzing data ( Y ) according to a given set of regressors often also referred to as predictors in the context of experiment design, (X) by estimating fit coefficients (). The general equation of the GLM is: $latex Y=Xbeta + varepsilon$. For fNIRS, Y is the time series of fNIRS measurements, X is the design matrix including all regressors, is the coefficients to be estimated for each regressor, and is the the error term which is assumed to be spherical Gaussian N(0,). The model is typically solved via generalized ordinary least squares:

**Alternative definition:**

**Synonym: **

**References:** https://doi.org/10.1007/978-1-59745-530-5_10

**Related terms:** Linear regression, Generalized Linear Mixed Model, regression