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Updated: Jan 9, 2026

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

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通过多模式和软面部电生理学对目标认知负载量化进行量化.

Dvir Teitelbaum, Yael Hanein

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

概括

可穿戴面部电肌图 (EMG) 提供了一种新的方法,可以在运动期间客观地测量认知负载. 这项技术在各种应用中显示出对实时工作负载监控的前景.

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

背景情况:

测量认知负载是困难的,因为它的动态性质和依赖主观方法.
传统的客观测量方法,如脑电图 (EEG),对工件和实验室环境都很敏感.

研究的目的:

开发和验证一个可穿戴的多模式系统,用于客观的认知负载评估.
探索面部肌电图 (EMG) 和EEG在自然条件下实时监测的潜力.

主要方法:

一个可穿戴的面部电极阵列系统在迷宫导航和N-back任务中记录了EEG和面部EMG信号.
诱导了认知负载,生理信号与主观负载得分相关联.

主要成果:

面部特定EMG通道与主观认知负载有很强的相关性,作为实时工作负载标记.
随着时间的推移,EEGβ度下降,这表明认知适应.
跨学科的变化凸显了个性化建模的必要性.

结论:

可穿戴面部EMG提供了一种可行的,非侵入性的实时认知负载评估方法.
这种方法在人机交互,神经机能学,适应性学习和监测认知疲劳和障碍的临床环境中具有潜在的应用.

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