Abstract: In the task of epileptic EEG signal detection,deep learning methods exhibit outstanding feature representation capabilities but suffer from poor interpretability.In contrast,the Takagi-Sugeno-Kang(TSK) fuzzy classifier is endowed with superior fuzzy-rule based interpretability,yet is hambered by its limited modeling ability.To well balance the performance and interpre-tability when deal with EEG signals,this paper proposes a Multi-scale Multi-granularity Decoupled Distillation TSK Fuzzy Classifier(MMDD-TSK-FC).Firstly,training one-dimensional Convolutional Neural Networks with different kernel sizes as teacher models enables comprehensive extraction of EEG features at multiple scales.Next,soft labels are generated by softening the outputs of the teacher models,and the Kullback-Leibler divergence between these soft labels and the outputs of TSK fuzzy classifiers with varying rule numbers is minimized to facilitate deep feature representation knowledge transfer.Meanwhile,the cross-entropy loss between the student model’s output and the ground-truth labels is minimized.Finally,the outputs of multiple TSK fuzzy classifiers are integrated using a voting mechanism.Meanwhile,the multi-granularity TSK fuzzy classifiers generate multiple sets of IF-THEN rules with varying levels of complexity,providing interpretable reasoning to support the model’s detection decisions.The experimental results on the Bonn and New Delhi HauzKhas epileptic EEG datasets thoroughly validate the superiority of the MMDD-TSK-FC,which improves accuracy by approximately 5% compared to the classical TSK classifier and outperforms other deep knowledge distillation models by around 3%.

Key words: Takagi-Sugeno-Kang fuzzy classifier, Epileptic EEG signal detection, Multi-scale, Multi-granularity, Knowledge distillation, Interpretability