This competition asked to build machine learning models in Microsoft Cortana Intelligence Suite to decode perceptions of human subjects from brain, specifically Electrocorticographic (ECoG) signals. The learning model needs to predict whether the human subject is seeing a house image or a face image from the ECoG signals collected from the subtemporal cortical surfaces of four seizure patients.

For this challenge, I used a blend of 5 different models, 2 dedicated to detection of evoked potential, and 3 to induced activity. No preprocessing or artifact rejection has been applied and Most of these models are based on my work on Riemannian Geometry.