Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah 26666, United Arab Emirates.
Department of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates.
Sensors (Basel). 2021 Jul 26;21(15):5043. doi: 10.3390/s21155043.
Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contains rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Accordingly, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.
精神压力是导致许多健康问题的严重因素之一。科学家和医生已经开发出各种工具来在早期评估精神压力水平。文献中已经提出了几种神经影像学工具来评估工作场所的精神压力。脑电图（EEG）信号是一个重要的候选者，因为它包含有关精神状态和状况的丰富信息。在本文中，我们回顾了现有的用于评估精神压力的 EEG 信号分析方法。该综述强调了研究结果之间的关键差异，并认为数据分析方法的变化导致了几个相互矛盾的结果。结果的变化可能是由于各种因素造成的，包括缺乏标准化协议、感兴趣的脑区、应激类型、实验持续时间、适当的 EEG 处理、特征提取机制以及分类器的类型。因此，与精神压力识别相关的重要部分是选择最合适的特征。特别是，包括时变、功能和动态脑连接在内的复杂多样的 EEG 特征，需要整合各种方法来了解它们与精神压力的关联。因此，该综述建议将皮质激活与连通性网络测量和深度学习方法融合，以提高精神压力水平评估的准确性。
