ArticleFrontiers in neuroscience2020
An Intelligence EEG Signal Recognition Method via Noise Insensitive TSK Fuzzy System Based on Interclass Competitive Learning.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 10 citations in OpenAlex.
- Optimized Projection and Fisher Discriminative Dictionary Learning for EEG Emotion Recognition.Frontiers in psychology · 2021Article
- A Domain Adaptation Sparse Representation Classifier for Cross-Domain Electroencephalogram-Based Emotion Classification.Frontiers in psychology · 2021Article
- Noise Robustness Low-Rank Learning Algorithm for Electroencephalogram Signal Classification.Frontiers in neuroscience · 2021Article
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Authors and funding
3 authors at 1 institution in 1 country.
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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
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