Multicenter Knowledge Transfer Calibration With Rapid Zeroth-Order TSK Fuzzy System for Small Sample Epileptic EEG Signals

作者：Chuang Wang, Pengjiang Qian, Zhihuang Wang, Weiwei Cai, Jian Yao, Yizhang Jiang, Xiangyu Yan, Wenjun Hu · 发表于：IEEE Transactions on Fuzzy Systems · 年份：2024 · DOI：10.1109/tfuzz.2024.3443878 · 被引用次数：29 · 研究领域：EEG and Brain-Computer Interfaces、Neural Networks and Applications

The diagnosis and treatment of epilepsy necessitate the precise identification and classification of electroencephalogram (EEG) signals. However, EEG samples from different medical institutions often exhibit variability due to factors such as institutional characteristics, geographic locations, and the professional levels of physicians. This variability limits the widespread application of existing methods in small or single medical institutions, as they typically rely on large-scale and high-quality datasets. To rapidly assist small or single medical institutions in constructing models for the diagnosis and classification of epileptic EEG signals that are both highly generalizable and interpretable while ensuring patient privacy, this article proposes an innovative learning framework named Multicenter Knowledge Transfer Calibration with rapid zeroth-order TSK fuzzy system (MKTC-R0T). This method employs the zeroth-order TSK fuzzy system as the baseline model for each center and integrates a knowledge transfer calibration strategy within a multicenter learning framework, aiming to enhance the model's generalizability and classification accuracy in the face of inconsistent sample quality and sample heterogeneity. Specifically, MKTC-R0T first establishes a base center model in a large medical institution, and then, by imitating the forgetting mechanism of the human brain, a portion of the knowledge at the base center is randomly forgotten, while the remaining knowledge is utili...