Abstract
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 c-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.
Highlights
Epilepsy occurs randomly and may occur multiple times in a day
possibilistic c-means (PCM) clustering is directly used on whole datasets or on samples in each class, and the antecedent parameters are learned using the obtained clustering results
Based on the Bayesian framework, we propose the possibilistic clustering in Bayesian with interclass competitive learning
Summary
Epilepsy occurs randomly and may occur multiple times in a day. In the case of epileptic seizures, the patients have a sudden physical convulsions and loss of consciousness, which bring great physical and psychological pain to patients (Ahmadlou and Adeli, 2011; Gummadavelli et al, 2018; Cury et al, 2019). The unsupervised nature of PCM makes it unable to use the class label information of samples, which causes the insufficient fuzzy space partition, further affecting the learning of antecedent parameters of fuzzy rules. PCM clustering is directly used on whole datasets or on samples in each class, and the antecedent parameters are learned using the obtained clustering results. We first propose a noise-insensitive possibilistic clustering in Bayesian framework with interclass competitive learning called PCB-ICL. Inherited by PCB, PCB-ICL is noise insensitive; different classes of cluster centers will produce a competitive relationship during the learning process. PCB-ICL integrates the competitive learning mechanism of clustering centers among different classes in the Bayesian framework. We obtain the antecedent part of fuzzy rules by performing PCB-ICL alternatively on each class samples.
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