Abstract / truncated to 115 words (read the full abstract)

Functional near infrared spectroscopy (fNIRS) needs a standardization in signal processing tools before it is recognized as a reliable neuroimaging modality. This thesis study tries to present a comprehensive analysis of the feasibility of applying statistical inference methods to fNIRS signals. Using hierarchical linear models, both classical and Bayesian techniques are pursued and performance of different methods are presented on a comparative basis. The results obtained from a set of cognitive signals show that fNIRS can identify cognitive activity both at the subject and group levels. The analysis suggests that mixed or Bayesian hierarchical models are especially convenient for fNIRS signals. A related problem that is discussed in this thesis study is to guarantee that ... toggle 6 keywords

functional near infrared spectroscopy – statistical inference – bayesian statistics – general linear model – constrained estimation – complexity.

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