Abstract
Transfer learning based method, which utilizes plenty labeled data in the source domain to build an accuracy classifier for the target domain, serves as an effective means in the epileptic detection by using electroencephalogram (EEG) signals. Among existing approaches, Fuzzy logic system (FLS) based on transductive transfer learning is an efficient method due to its superior interpretability and strong learning abilities. However, this kind of method cannot simultaneously reduce the differences in both marginal distributions and conditional distributions between the training and test datasets of EEG signals. To overcome this problem, in this paper, we construct a Takagi-Sugeno-Kang (TSK) FLS based on the joint distribution adaptation (JDA), which refers to TSK-JDA-FLS. It aims to match both marginal and conditional distributions, and we extend the algorithm to perform a multi-class classification for identifying epileptic EEG signals. Extensive experiments verify that TSK-JDA-FLS significantly outperforms competitive non-transfer learning and transfer learning methods in the epileptic EEG datasets.