Time-variant spatial filtering for motor imagery classification

Effective spatial filtering plays a key role in motor imagery classification. This paper presents a novel approach to spatial filtering of EEG signal by modelling time-variant spatial patterns. This is in contrast to conventional Common Spatial Pattern which assumes static spatial patterns in a motor imagery trial. We define the model such that it accounts for relatively higher order dynamics in EEG. Furthermore, we formulate the training of the model as a dual optimization problem, and we derive an iterative optimization algorithm using quadratically constrained quadratic programming. Our experimental results on healthy subjects indicates that the proposed method is able to produce higher classification accuracy.