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基于相关性的道选择用于使用EEG信号进行认知工作负载评估和分类

Armin Ghasimi¹, Sina Shamekhi²

¹Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran.

Physical and engineering sciences in medicine

|November 3, 2025

概括

使用脑电图 (EEG) 信号估计认知工作量通过选择关键的额头通道和采用先进的时间频率分解方法来改进. 这种方法提高了准确性,并减少了实时应用程序的复杂性.

科学领域:

神经科学是一个神经科学.
认知科学认知科学
生物医学工程生物医学工程

背景情况:

认知工作负载,任务中的精神努力,影响日常决策和效率.
电脑电图 (EEG) 信号提供了一种可靠的,非侵入性的方法来测量认知工作负载.
准确的认知工作负载估计对于优化性能和最小化错误至关重要.

研究的目的:

使用EEG信号对认知工作负载水平进行分类.
通过道选择来降低实时应用程序的计算复杂性.
评估时间频率分解技术的有效性,以评估工作负载.

主要方法:

使用皮尔森相关系数进行道选择.
通过最大重叠离散波形变换 (MODWT) 和经验模式分解 (EMD) 进行时间频率分解.
使用改进距离评估提取和选择特征,然后用SVM,K-最近邻居和决策树进行分类.

主要成果:

额头EEG通道被确定为认知工作负载评估的关键.
MODWT和EMD与支持矢量机 (SVM) 分类器的组合实现了最高的准确性.
最佳的频道选择和时间频率方法显著提高了分类准确性,同时降低了系统复杂性.

结论:

额头EEG通道对于认知工作量估计具有高度信息性.
混合时间频率分解 (MODWT-EMD) 与SVM相结合,为工作负载分类提供了一个强大的方法.
这种方法提高了实时认知工作负载监控的准确性和效率.

关键词:

认知工作负载的认知工作负载.经验模式分解分解改进了距离评估功能.最大重叠离散波形变换最大重叠.皮尔森相关系数的相关系数

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