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TriPat‑XFE: a triangle pattern‑based explainable feature engineering framework for EEG classification

delete2025-12-05
delete0
PRE
AI
Ş
Şüheda Kaya
İ
İrem Taşçı
P
Prabal Datta Barua
G
Gülay Taşçı
Ş
Şengül Doğan
T
Türker Tuncer
R
Ru‐San Tan
U
U. Rajendra Acharya
DOI：10.1016/j.neuroscience.2025.12.004delete
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摘要

摘要

En 中文
• TriPat提出了一种基于线性三角形的EEG特征Transformer。 • TriPat-XFE在Stress和Psychosis数据集上实现了100%的准确率。 • DLob提供带有连接组图的符号EEG解释。 • CWINCA和tkNN增强了特征选择和分类稳定性。 • TriPat-XFE以低成本和高可解释性超越了SOTA。

期刊

Journal of Neuroscience 封面图
Journal of Neuroscience
IF:
4
论文数:
1.4K
被引数:
13.5W

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F
Firat University
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4.2K
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被引数: 43
U
University of Southern Queensland
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4.1K
论文数: 4.8K
被引数: 18
E
elazig fethi sekin city hospital
学者数:
37
论文数: 25
被引数: 0
N
National Heart Centre Singapore
学者数:
1.6K
论文数: 1.3K
被引数: 2.1K
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