Model-Based Source Separation for multi-class motor imagery

This paper presents a general framework to recover task-related sources from a multi-class Brain-Computer Interface (BCI) based on motor imagery. Our method gathers two common approaches to tackle the multi-class problem: 1) the supervised approach of Common Spatial Patterns and Sparse and/or Spectral variants (CSP, CSSP, CSSSP) to dis-criminate between different tasks; 2) the criterion of statis-tical independence of non-stationary sources used in Inde-pendent Component Analysis (ICA). Our method can exploit different properties of the signals to find the best discrimi-native linear combinations of sensors. This yields different models of separation. This work aims at comparing these models. We show that the use of a priori knowledge about the sources and the performed task increases classification rates compared to previous studies. This work gives a gen-eral framework to improve Brain-Computer Interfaces and to adapt spatial filtering methods to each subject. 1