Transfer discriminative dictionary learning with label consistency for classification of EEG signals of epilepsy

Abstract

EEG signal classification play an important role in recognition of epilepsy. Recently, dictionary learning algorithms have shown the effectiveness in this field. When designing dictionaries, due to highly non-stationary of EEG signals, and collecting signals existing in different stimulus and drug modes, training and testing scenarios may be different. Thus, the performance of classical dictionary learning algorithms is unsatisfactory. In this paper, a transfer discriminative dictionary learning with label consistency (called TDDLLC) algorithm is proposed for EEG signal classification. Since each EEG signal can be represented as a linear combination of dictionary atoms, and some atoms are dataset independent, two dictionaries are learned simultaneously in source domain (SD) and target domain (TD) respectively where the discrepancy between two dictionaries is minimized. Meanwhile, utilizing the label information of samples in SD and a small number of labeled samples in TD, these dictionaries are learned with the aim of achieving discriminative abilities. To avoid the NP-hard problem, $$\ell_{1}$$ -norm regularization term is used in TDDLLC, and objective function is solved by block-coordinate descent method. Extensive experiments have been performed on Bonn dataset and show the validity of the TDDLLC algorithm.