A maximum mutual information approach for constructing a 1D continuous control signal at a self-paced brain-computer interface

This paper addresses an important issue in a self-paced brain-computer interface (BCI): constructing subject-specific continuous control signal. To this end, we propose an alternative to the conventional regression/classification-based mechanism for building the transformation from EEG features into a univariate control signal. Based on information theory, the mechanism formulates the optimum transformation as maximizing the mutual information between the control signal and the mental state. We introduce a non-parametric mutual information estimate for general output distribution, and then develop a gradient-based algorithm to optimize the transformation using training data. We conduct an offline simulation study using motor imagery data from the BCI Competition IV Data Set I. The results show that the learning algorithm converged quickly, and the proposed method yielded significantly higher BCI performance than the conventional mechanism.