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Updated: Feb 24, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

使用非线性动态和图形理论EEG特征进行跨主体MCI检测的可解释特征转换器框架.

Hadi Azizpour Lindi, Reza Shalbaf, Ahmad Shalbaf

Research square

|February 23, 2026

概括

早期发现轻度认知障碍 (MCI) 对预防阿尔茨海默病 (AD) 至关重要. 这项研究表明,将EEG衍生的和图形特征与变压器网络相结合,可以有效地将MCI与健康对照区分开来.

科学领域:

神经科学是一个神经科学.
生物医学工程生物医学工程
人工智能的人工智能

背景情况:

轻度认知障碍 (MCI) 是阿尔茨海默病 (AD) 的前体,需要早期检测才能进行干预.
电脑电图 (EEG) 提供了一种非侵入性方法来评估与认知功能相关的大脑活动.
区分MCI与健康对照 (HC) 是具有挑战性的,但对于及时的治疗策略至关重要.

研究的目的:

评估基于和图形的EEG特征在区分MCI与HC的有效性.
将变压器网络的性能与使用这些工程功能的EEGNet模型进行比较.
利用可解释的深度学习来识别MCI早期检测的关键生物标志物.

主要方法:

使用了183名参与者的静止状态,闭眼EEG数据 (127名HC,56名MCI).
提取了五个频段的非线性动态测量 (,碎形维度,利亚普诺夫指数) 和图形理论连接特征.
将变压器网络和EEGNet模型应用于工程特征集进行分类.

主要成果:

基于特征的变压器模型实现了高分类准确率97.04% ± 0.72.72.
变压器模型的表现优于EEGNet基线,证明了富含功能的基于注意力的架构的优势.

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

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Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

SHAP分析确定了具有影响力的非线性和连接特征,以及关键的EEG通道,提高了模型的解释性.

结论:

将手工制作的EEG功能与变压器网络相结合,为早期MCI检测提供了一种强大且可解释的方法.
这种方法显示出开发先进的诊断工具以对抗阿尔茨海默病进展的巨大潜力.
基于特征的深度学习模型为了解和识别MCI等神经退行性疾病提供了有希望的途径.