Deep stacked support matrix machine based representation learning for motor imagery EEG classification

Electroencephalograph (EEG) classification is an important technology that can establish a mapping relationship between EEG features and cognitive tasks. Emerging matrix classifiers have been successfully applied to motor imagery (MI) EEG classification, but they belong to shallow classifiers, making powerful stacked generalization principle not exploited for automatically learning deep EEG features. To learn the high-level representation and abstraction, we proposed a novel deep stacked support matrix machine (DSSMM) to improve the performance of existing shallow matrix classifiers in EEG classification.

The main idea of our framework is founded on the stacked generalization principle, where support matrix machine (SMM) is introduced as the basic building block of deep stacked network. The weak predictions of all previous layers obtained via SMM are randomly projected to help move apart the manifold of the original input EEG feature, and then the newly generated features are fed into the next layer of DSSMM. The framework only involves an efficient feed-forward rather than parameter fine-tuning with backpropagation, each layer of which is a convex optimization problem, thus simplifying the objective function solving process.

Extensive experiments on three public EEG datasets and a self-collected EEG dataset are conducted. Experimental results demonstrate that our DSSMM outperforms the available state-of-the-art methods.

The proposed DSSMM inherits the characteristic of matrix classifiers that can learn the structural information of data as well as the powerful capability of deep representation learning, which makes it adapted to classify complex matrix-form EEG data.