Deep stacked least square support matrix machine with adaptive multi-layer transfer for EEG classification

Abstract

The recent extraordinary success of deep neural networks for electroencephalogram (EEG) decoding can be mainly attributed to the availability of large-scale labeled datasets. However, it requires prolonged EEG calibration, which is especially inconvenient for people with disabilities. Training deep neural networks with insufficient labeled EEG data will make the model suffer from limited generalization capability. In view of this, a new deep stacked network (DSN), called deep stacked transfer least square support matrix machine (DST-LSSMM), is proposed to alleviate the above issue. Following the philosophy of stacked generalization, the proposed DST-LSSMM utilizes least square support matrix machine (LSSMM) as its basic building unit and the random projection of previous layers as its stacking element. The adopted matrix classifier LSSMM directly models the matrix-form data, which is able to exploit the structural information between the columns or rows in EEG feature matrices to further improve the generalization capability of the model. Besides, we design an adaptive multi-layer model knowledge transfer learning scheme to guarantee consistency across the interrelated layers. The proposed adaptive multi-layer transfer scheme allows the use of model knowledge of previous related layers to facilitate the model construction at higher layers, consequently enhancing the generalization capability of the deep stacked model. DST-LSSMM is carried out in an efficient feed-forward way without parameter pre-training and fine-tuning, which can simplify the model optimization process. We extensively evaluate our method on three EEG datasets. Experimental results demonstrate that our method achieves promising performances on EEG classification.