Recognition of Multiclass Epileptic EEG Signals Based on Knowledge and Label Space Inductive Transfer

Electroencephalogram (EEG) signal recognition based on machine learning models is becoming more and more attractive in epilepsy detection. For multiclass epileptic EEG signal recognition tasks including the detection of epileptic EEG signals from different blends of different background data and epilepsy EEG data and the classification of different types of seizures, we may perhaps encounter two serious challenges: (1) a large amount of EEG signal data for training are not available and (2) the models for epileptic EEG signal recognition are often so complicated that they are not as easy to explain as a linear model. In this paper, we utilize the proposed transfer learning technique to circumvent the first challenge and then design a novel linear model to circumvent the second challenge. Concretely, we originally combine γ -LSR with transfer learning to propose a novel knowledge and label space inductive transfer learning model for multiclass EEG signal recognition. By transferring both knowledge and the proposed generalized label space from the source domain to the target domain, the proposed model achieves enhanced classification performance on the target domain without the use of kernel trick. In contrast to the other inductive transfer learning methods, the method uses the generalized linear model such that it becomes simpler and more interpretable. Experimental results indicate the effectiveness of the proposed method for multiclass epileptic EEG signal recognition.