Fused Group Lasso: A New EEG Classification Model With Spatial Smooth Constraint for Motor Imagery-Based Brain–Computer Interface

摘要

The traditional group sparse optimization method can simultaneously achieve the channel selection and classification for the motor imagery electroencephalogram (EEG) signals, but it doesn’t consider the spatial structure information between the electrode channels. Combining the group sparsity and spatial smoothness of EEG signals, a new EEG classification model is proposed, which is an improvement of group least absolute shrinkage and selection operator (LASSO). We call it fused group LASSO. First, group LASSO is used to model the group sparsity of EEG signals, the features of the same channel are assigned the same weights. Then, based on group LASSO, channel weights are regularized by total variation norm (TV-norm), which constrains the weights of adjacent channels to the same or similar, thereby the spatial smoothness modeling of EEG signals can be achieved. Using the primal-dual theory, an optimization algorithm for the new model is given. In order to verify the effectiveness of the new model, experiments were performed on two public brain-computer interface (BCI) competition data sets and one self-collected data set. Compared with the existing sparse optimization methods, the proposed method has achieved the highest average classification accuracy of 79.24%, 86.64% and 81.09%, respectively, and with better physiological interpretability. Compared with spatial filtering methods with smooth constraints, the proposed method realized global spatial smooth in a data-driver manner, and achieved the highest average classification accuracy of 84.96% in two competition data sets. All the experimental results showed that the proposed method can significantly improve the performance of BCI systems.