EEG workload estimation and classification: a systematic review.

Jahid Hassan, Md Shamim Reza, Syed Udoy Ahmed, Nazmul Haque Anik, Md Obaydullah Khan
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Abstract

Objective: Electroencephalography (EEG) has evolved into an indispensable instrument for estimating cognitive workload in various domains. ML and DL techniques have been increasingly employed to develop accurate workload estimation and classification models based on EEG data. The goal of this systematic review is to compile the body of research on EEG workload estimation and classification using ML and DL approaches.

Methods: The PRISMA procedures were followed in conducting the review, searches were conducted through databases at SpringerLink, ACM Digital Library, IEEE Explore, PUBMED, and Science Direct from the beginning to the end of February 16, 2024. Studies were selected based on predefined inclusion criteria. Data were extracted to capture study design, participant demographics, EEG features, ML/DL algorithms, and reported performance metrics.

Results: Out of the 125 items that emerged, 33 scientific papers were fully evaluated. The study designs, participant demographics, and EEG workload measurement and categorization techniques used in the investigations differed. SVM, CNN, and hybrid networks are examples of ML and DL approaches that were often used. Analyzing the accuracy scores achieved by different ML/DL models. Furthermore, a relationship was noted between sample frequency and model accuracy, with higher sample frequencies generally leading to improved performance. The percentage distribution of ML/DL methods revealed that SVMs, CNNs, and RNNs were the most commonly utilized techniques, reflecting their robustness in handling EEG data.

Significance: The comprehensive review emphasizes how ML may be used to identify mental workload across a variety of disciplines using EEG data. Optimizing practical applications requires multimodal data integration, standardization efforts, and real-world validation studies. These systems will also be further improved by addressing ethical issues and investigating new EEG properties, which will improve human-computer interaction and performance assessment.

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脑电图工作量估算与分类:系统综述。
目的:脑电图(EEG)已发展成为估算各领域认知工作量不可或缺的工具。人们越来越多地采用 ML 和 DL 技术来开发基于脑电图数据的精确工作量估算和分类模型。本系统性综述的目的是汇编使用 ML 和 DL 方法进行脑电图工作量估算和分类的研究成果:方法:在进行综述时遵循了 PRISMA 程序,并在 SpringerLink、ACM Digital Library、IEEE Explore、PUBMED 和 Science Direct 等数据库中进行了搜索,搜索时间从开始到 2024 年 2 月 16 日结束。根据预定义的纳入标准选择研究。提取的数据包括研究设计、参与者人口统计学特征、脑电图特征、ML/DL 算法以及报告的性能指标:在出现的 125 个项目中,有 33 篇科学论文得到了全面评估。研究设计、参与者人口统计学特征、脑电图工作量测量以及调查中使用的分类技术各不相同。SVM、CNN 和混合网络是经常使用的 ML 和 DL 方法。分析不同 ML/DL 模型达到的准确度分数。此外,我们还注意到样本频率与模型准确度之间的关系,样本频率越高,性能通常越好。ML/DL 方法的百分比分布显示,SVMs、CNNs 和 RNNs 是最常用的技术,反映了它们在处理脑电图数据时的鲁棒性:这篇综合评论强调了如何利用脑电图数据将 ML 用于识别各学科的心理工作量。优化实际应用需要多模态数据整合、标准化工作和真实世界验证研究。这些系统还将通过解决伦理问题和研究新的脑电图特性得到进一步改进,从而改善人机交互和绩效评估。
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