Abstract:Abstract: The key to recognize epileptic EEG signals is to obtain effective features and construct an interpretable classifier. In this paper, a novel Takagi-Sugeno-Kang (TSK) fuzzy classifier based on enhanced deep feature (ED-TSKFC) for epilepsy EEG signal recognition is proposed. ED-TSK-FC adopts an one-dimension convolution neural network (1D-CNN) to extract deep feature and label information, label information is expanded to deep feature space to generate enhanced deep feature; As the antecedent-consequent variable of ED-TSK-FC, enhanced deep feature can provide deep feature and label information for each fuzzy rule, hence fuzzy rules open the black box of 1D-CNN in an interpretable manner; In addition, ED-TSK-FC also formulates a cheap strategy to decrease training time; Finally, experiments show that this method provides an excellent performance on the Bonn Epilepsy Dataset in terms of classification performance, training time and interpretability.