Signal Processing and Biomedical Applications (SiPBA)
Our research is intended to create bridges between statistics/engineering and medicine. We develop machine learning methods to explore biomedical data, with a strong emphasis in neuroimaging. Currently, we are exploring new paths associated with time series such as EEG, functional imaging including fMRI, PET and SPECT, while maintaining our traditional lines involving structural MRI. We have succesfully applied many methodologies to evaluate the progression Alzheimer’s Disease or Parkinson’s Disease, establishing subgroups of Autism and, recently, to classify breast cancer MRI imaging.