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An Intelligence EEG Signal Recognition Method via Noise Insensitive TSK Fuzzy System Based on Interclass Competitive

Tongguang Ni¹, Xiaoqing Gu¹, Cong Zhang¹

¹School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China.

Frontiers in Neuroscience

|October 5, 2020

Summary

A new fuzzy system, PCB-ICL-TSK, enhances electroencephalogram (EEG) signal recognition for epilepsy research. This noise-insensitive system improves accuracy by learning from competitive data clusters, aiding in better diagnosis.

Keywords:

Bayesian framework Ho–Kashyap procedure TSK fuzzy system asymmetric expectile term noise insensitive possibilistic clustering

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Area of Science:

Biomedical Engineering
Computational Neuroscience
Artificial Intelligence

Background:

Epilepsy is a neurological disorder characterized by abnormal brain neuron discharge.
Electroencephalogram (EEG) is crucial for epilepsy research and diagnosis.
Existing EEG analysis methods can be sensitive to noise, impacting recognition accuracy.

Purpose of the Study:

To propose a novel noise-insensitive Takagi-Sugeno-Kang (TSK) fuzzy system for improved EEG signal recognition.
To enhance the robustness and interpretability of fuzzy rules for EEG data analysis.
To develop a system that effectively identifies epileptic seizures from EEG signals.

Main Methods:

A possibilistic clustering in Bayesian framework with interclass competitive learning (PCB-ICL) was developed to determine fuzzy rule antecedent parameters.
PCB-ICL utilizes a competitive learning approach, attracting cluster centers to same-class samples while repelling them from dissimilar data.
The Metropolis-Hastings method and an alternating iterative strategy were employed for optimal clustering and interpretable parameters. Asymmetric expectile terms and the Ho-Kashyap procedure were used for consequent parameter learning.

Main Results:

The proposed PCB-ICL algorithm demonstrated noise insensitivity in determining fuzzy rule antecedents.
The developed PCB-ICL-TSK system exhibited high interpretability of learned parameters.
Comprehensive experiments on real-world EEG data confirmed the robust and effective performance of the PCB-ICL-TSK system for EEG signal recognition.

Conclusions:

The novel PCB-ICL-TSK fuzzy system offers a noise-insensitive and effective approach for EEG signal recognition in epilepsy research.
The interclass competitive learning strategy enhances the system's ability to handle complex EEG data.
This method provides a promising tool for improving the accuracy and reliability of epilepsy diagnosis through EEG analysis.