Machine learning algorithms require a large and diverse data set for robust training. However, gathering a sufficient number is a difficult task due to time and budget constraints. Generated data sets can augment training data and provide diverse example for training. We propose a method to generate realistic diffuse optical tomography (DOT) data sets based on known physiological components of the DOT signal. We generate three dimensional models of each signal component and seed the hemodynamic response to activate targeted cortices. Our method reduces the need for a large recruitment process and increases the accuracy of machine learning algorithms.
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