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评估认知工作负载模型的性能,除了EEG外,还包括主观认可

Yamini Gogna¹, Sheela Tiwari², Rajesh Singla²

¹ICE Department, Dr. B. R. Ambedkar NIT Jalandhar, GT Road Bye-Pass, Jalandhar, Punjab, 144008, India. gognayaminiice@gmail.com.

Medical & biological engineering & computing

|March 3, 2024

概括

这项研究引入了一种新的脑电图 (EEG) 驱动的机器学习模型,用于评估在能力练习期间的认知负载. 该模型准确地区分工作负载水平,有助于课程优化.

科学领域:

认知科学认知科学
神经科学是一个神经科学.
人与计算机的交互

背景情况:

认知负载是以能力为导向的任务固有的,由于测量的复杂性,对准确的评估提出了挑战.
之前的研究没有同时报告生理学 (EEG) 和主观 (NASA-TLX) 认知负载的测量.
在操作员工作负载中普遍存在的内在认知负载需要有效的评估方法.

研究的目的:

开发和验证一种新的基于脑电图 (EEG) 的机器学习模型,用于认知负载评估.
将支持矢量机 (SVM) 与NASA的任务负载指数 (NASA-TLX) 集成,以进行全面的工作负载探索.
建立一种可靠的方法来区分操作员在能力任务期间的认知负载水平.

主要方法:

利用认知负载理论 (CLT) 来设计一个内在的"发现差异"任务.
开发了一种电脑学 (EEG) 驱动的支持向量机 (SVM) 模型,用于认知负载分类.
使用NASA的任务负载指数 (NASA-TLX) 进行主观工作负载评估和模型验证.

主要成果:

SVM模型实现了三级分类准确度,范围从85.4%到97.4% (p < 0.05).
美国宇航局-TLX评级也显示了高分类准确度,从88.33%到97.33%.
神经测量指数在认知负载水平 (p < 0.05) 中显著不同,NASA-TLX显示出良好的有效性和可靠性 (克朗巴赫的α = 0.753).

结论:

开发的EEG驱动的SVM模型为评估操作员认知负载提供了准确可靠的方法.
这种方法可以帮助识别和逐步消除导致负载不足或负载过重的特定认知练习.
综合生理和主观测量为优化培训课程和工作负载管理提供了强大的工具.

关键词:

电脑电图 (EEG) 是一种电脑电图.心理工作负荷 (MWL)美国宇航局的任务负载指数 (NASA-TLX)支持矢量机器 (SVM) 是一个支持矢量机器.

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