Li Ziyang, Song Jianing, Wang Hong, Li Tan, Gouda Mohamed Amin, Gong Jiale
Department of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang, 110819, Liaoning, China.
The Future Laboratory, Tsinghua University, Haidian district, Shenyang, 100084, Beijing, China.
Med Biol Eng Comput. 2025 Sep 12. doi: 10.1007/s11517-025-03424-9.
Middle-aged people generally experience greater work pressure but higher health risks. However, the existing EEG-based cognitive load monitoring research has paid less attention to this segment of the population. We investigated high temporal resolution decoding of cognitive load from EEG signals in middle-aged individuals during inhibition and updating tasks. In this paper, we employed publicly available EEG data from Multi-Source Interference Task (MSIT) and Sternberg Memory Task (STMT) paradigms to examine variations in brain activation modes and cognitive load under low and high cognitive demands. This analysis was conducted using time courses of event-related potential (ERP) scalp maps. To validate the effect of the method, we conducted multivariate pattern recognition and statistics analysis. The point-by-point classification accuracy sequences obtained from decoding were assessed for significance above chance levels using one-tailed t-tests, with corrections for multiple comparisons made via the false discovery rate (FDR) method. After comparative analysis, we found that the decoder was more effective in categorizing different tasks, while the MSIT was better than STMT's in categorizing cognitive loads. In addition, we also analyzed the spatio-temporal properties of brain activation under different conditions, which is instrumental in developing more powerful classifiers. Additionally, group-level statistical comparisons were performed to explore how AD risk may influence cognitive load decodings. The study results show that this program is feasible and can be used in the future to monitor the workload of high-risk job operators in real time and longitudinal observation in medical diagnostics.
中年人通常承受着更大的工作压力，但健康风险也更高。然而，现有的基于脑电图（EEG）的认知负荷监测研究对这一人群的关注较少。我们研究了在抑制和更新任务期间，从中年个体的EEG信号中进行高时间分辨率的认知负荷解码。在本文中，我们使用了来自多源干扰任务（MSIT）和斯特恩伯格记忆任务（STMT）范式的公开可用EEG数据，以检查在低认知需求和高认知需求下大脑激活模式和认知负荷的变化。这种分析是使用事件相关电位（ERP）头皮图的时间进程进行的。为了验证该方法的效果，我们进行了多变量模式识别和统计分析。使用单尾t检验评估从解码中获得的逐点分类准确率序列是否显著高于机遇水平，并通过错误发现率（FDR）方法对多重比较进行校正。经过比较分析，我们发现解码器在对不同任务进行分类时更有效，而MSIT在对认知负荷进行分类时比STMT更好。此外，我们还分析了不同条件下大脑激活的时空特性，这有助于开发更强大的分类器。此外，还进行了组水平的统计比较，以探讨阿尔茨海默病（AD）风险如何影响认知负荷解码。研究结果表明，该程序是可行的，未来可用于实时监测高风险工作操作员的工作量，并在医学诊断中进行纵向观察。
