Uncued Brain-Computer Interfaces: a Variational Hidden Markov Model of Mental State Dynamics

摘要

This paper describes a method to improve uncued Brain-Computer Interfaces based on motor imagery. Our algorithm aims at filtering the continuous classifier output by incorporating prior knowledge about the mental state dynamics. On dataset IVb of BCI competition III, we compare the performances of four different methods by combining smoothed probabilities filtered by our algorithm/direct classifier output and static/dynamic classifier. We demonstrate that the combination of our algorithm with a dynamic classifier yields the best results.