Our group develops statistical machine learning algorithms to analyze fMRI data. We are specifically interested in algorithms that can learn to identify and track the cognitive processes that give rise to observed fMRI data.
Example: In one fMRI study we trained our algorithms to decode whether the words being read by a human subject were about tools, buildings, food, or several other semantic categories. The trained classifier is 90% accurate, for example, discriminating whether the subject is reading words about tools or buildings.
The following figure shows, for each of three different subjects, the degree to which different brain locations can help predict the word's semantic category. Red and yellow voxels are most predictive. Note the most predictive regions in different subjects are in similar locations.