Mixture Models for Cognitive Brain State Prediction

We dealt with the challenge of cognitive brain state prediction as proposed by the Pittsburgh Brain Activity Interpretation Competition (PBAIC) 2007. The problem was decomposed in many subsequent steps: pre-processing, feature selection, learning model selection, model training, and post-processing. We investigated the steps combining unsupervised and supervised learning techniques and assessed the most effective technique to permorm each of them. The final predictions have been produced by using a mixture of different learning models: k-means, recurrent neural networks, gaussian process regression, iterated conditional mean.