Autoregressive models for biomedical signal processing. (arXiv:2304.11070v1 [eess.SP])

Autoregressive models are ubiquitous tools for the analysis of time series in
many domains such as computational neuroscience and biomedical engineering. In
these domains, data is, for example, collected from measurements of brain
activity. Crucially, this data is subject to measurement errors as well as
uncertainties in the underlying system model. As a result, standard signal
processing using autoregressive model estimators may be biased. We present a
framework for autoregressive modelling that incorporates these uncertainties
explicitly via an overparameterised loss …

algorithm analysis arxiv autoregressive model autoregressive models biomedical biomedical engineering brain brain activity computational data engineering errors example framework function loss measurement modelling neuroscience processing series signal standard time series tools