  We introduce a rapid and precise analytical approach for analyzing cerebral
blood flow (CBF) using Diffuse Correlation Spectroscopy (DCS) with the
application of the Extreme Learning Machine (ELM). Our evaluation of ELM and
existing algorithms involves a comprehensive set of metrics. We assess these
algorithms using synthetic datasets for both semi-infinite and multi-layer
models. The results demonstrate that ELM consistently achieves higher fidelity
across various noise levels and optical parameters, showcasing robust
generalization ability and outperforming iterative fitting algorithms. Through
a comparison with a computationally efficient neural network, ELM attains
comparable accuracy with reduced training and inference times. Notably, the
absence of a back-propagation process in ELM during training results in
significantly faster training speeds compared to existing neural network
approaches. This proposed strategy holds promise for edge computing
applications with online training capabilities.
