Cross-domain EEG signal classification via geometric preserving transfer discriminative dictionary learning

EEG signal classification is a key technology for EEG signal processing and identification systems. Dictionary learning has shown excellent performance due to its sparse representation and learning capability. Usually dictionary learning requires sufficient labeled EEG signals to build classification models, and assumes that the data distribution of training and test signals are the same. However, in new EEG signal domain, often only a small amount of signals are labeled, and more are not labeled. At the same time, data dynamicity, confounding factors and strong interclass similarity also seriously disrupt the performance of EEG signal classifier. To this end, a geometric preserving transfer discriminative dictionary learning method called GPTDDL is developed for cross-domain EEG signal classification. Through projected signals of different domains to the common subspace, a shared discriminative dictionary is obtained, which explores the geometric structure information by graph Laplacian regularization and discriminative information by principal component analysis regularization. Benefiting from the discriminative information transferred from source domain, the discriminability of the learned sparse coding of target domain is strengthened. GPTDDL integrates this idea into the framework of LC-KSVD, and learns the subspace and dictionary learning parameters in an iterative strategy. The experimental results on Bonn EEG signal dataset demonstrate the validity of the GPTDDL method.