Ni Tongguang, Gu Xiaoqing, Zhang Cong
School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China.
Front Neurosci. 2020 Sep 4;14:837. doi: 10.3389/fnins.2020.00837. eCollection 2020.
Epilepsy is an abnormal function disease of movement, consciousness, and nerve caused by abnormal discharge of brain neurons in the brain. EEG is currently a very important tool in the process of epilepsy research. In this paper, a novel noise-insensitive Takagi-Sugeno-Kang (TSK) fuzzy system based on interclass competitive learning is proposed for EEG signal recognition. First, a possibilistic clustering in Bayesian framework with interclass competitive learning called PCB-ICL is presented to determine antecedent parameters of fuzzy rules. Inherited by the possibilistic -means clustering, PCB-ICL is noise insensitive. PCB-ICL learns cluster centers of different classes in a competitive relationship. The obtained clustering centers are attracted by the samples of the same class and also excluded by the samples of other classes and pushed away from the heterogeneous data. PCB-ICL uses the Metropolis-Hastings method to obtain the optimal clustering results in an alternating iterative strategy. Thus, the learned antecedent parameters have high interpretability. To further promote the noise insensitivity of rules, the asymmetric expectile term and Ho-Kashyap procedure are adopted to learn the consequent parameters of rules. Based on the above ideas, a TSK fuzzy system is proposed and is called PCB-ICL-TSK. Comprehensive experiments on real-world EEG data reveal that the proposed fuzzy system achieves the robust and effective performance for EEG signal recognition.
癫痫是一种因大脑神经元异常放电而导致的运动、意识和神经功能异常的疾病。脑电图（EEG）目前是癫痫研究过程中非常重要的工具。本文提出了一种基于类间竞争学习的新型抗噪声高木-关野-康（TSK）模糊系统用于脑电信号识别。首先，提出了一种在贝叶斯框架下基于类间竞争学习的可能性聚类方法，称为PCB-ICL，用于确定模糊规则的前件参数。继承了可能性均值聚类的特点，PCB-ICL对噪声不敏感。PCB-ICL在竞争关系中学习不同类别的聚类中心。得到的聚类中心被同一类别的样本吸引，同时被其他类别的样本排斥并远离异类数据。PCB-ICL使用Metropolis-Hastings方法以交替迭代策略获得最优聚类结果。因此，所学习到的前件参数具有较高的可解释性。为了进一步提高规则的抗噪声能力，采用非对称期望分位数项和Ho-Kashyap过程来学习规则的后件参数。基于上述思想，提出了一种TSK模糊系统，称为PCB-ICL-TSK。对真实脑电数据的综合实验表明，所提出的模糊系统在脑电信号识别方面具有稳健且有效的性能。
