Uncertainty quantification plays an important role in biomedical engineering
as measurement data is often unavailable and literature data shows a wide
variability. Using state-of-the-art methods one encounters difficulties when
the number of random inputs is large. This is the case, e.g., when using
composite Cole-Cole equations to model random electrical properties. It is
shown how the number of parameters can be significantly reduced by the
Karhunen-Loeve expansion. The low-dimensional random model is used to quantify
uncertainties in the axon activation during deep brain stimulation. Numerical
results for a Medtronic 3387 electrode design are given.Comment: 4 pages, 5 figure
