A Novel Dual-Center-Based Intuitionistic Fuzzy Twin Bounded Large Margin Distribution Machines

Zhang, Libo; Jin, Qiang; Shuangyi, Fan; Liu, Dun

doi:10.1109/tfuzz.2023.3245215

Cited by 12 publications

(1 citation statement)

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“…The paper [151] proposes an innovative approach for the processing of EEG signals, based on the use of an intuitionistic fuzzy strategy in the context of double-center largemargin distribution machines. This methodology stands out for the integration of an intuitionistic fuzzy function that improves computational efficiency through a precise determination of the degree of non-membership of a sample, calculated based on the distance from the centers of two different categories.…”

Section: Threatsmentioning

confidence: 99%

EEG Data Analysis Techniques for Precision Removal and Enhanced Alzheimer’s Diagnosis: Focusing on Fuzzy and Intuitionistic Fuzzy Logic Techniques

Versaci,

La Foresta

2024

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Effective management of EEG artifacts is pivotal for accurate neurological diagnostics, particularly in detecting early stages of Alzheimer’s disease. This review delves into the cutting-edge domain of fuzzy logic techniques, emphasizing intuitionistic fuzzy systems, which offer refined handling of uncertainties inherent in EEG data. These methods not only enhance artifact identification and removal but also integrate seamlessly with other AI technologies to push the boundaries of EEG analysis. By exploring a range of approaches from standard protocols to advanced machine learning models, this paper provides a comprehensive overview of current strategies and emerging technologies in EEG artifact management. Notably, the fusion of fuzzy logic with neural network models illustrates significant advancements in distinguishing between genuine neurological activity and noise. This synthesis of technologies not only improves diagnostic accuracy but also enriches the toolset available to researchers and clinicians alike, facilitating earlier and more precise identification of neurodegenerative diseases. The review ultimately underscores the transformative potential of integrating diverse computational techniques, setting a new standard in EEG analysis and paving the way for future innovations in medical diagnostics.

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Section: Threatsmentioning

confidence: 99%