发表机构
Kazakh-British Technical University（哈英理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种融合脑电图、生理信号、行为背景和自我报告的多模态框架，用于追踪日常认知活动，通过试点研究验证了可行性，并揭示了动机与唤醒度、注意力等的强相关性及时间依赖模式。
AI 中文摘要
理解日常生活中的人类认知活动仍然具有挑战性，因为认知具有动态性、情境依赖性和多模态性。基于实验室的研究往往无法捕捉真实世界的认知过程，而单一模态的方法只能提供对认知状态的部分洞察。可穿戴传感技术的进步现在使得收集异构数据流成为可能，从而更全面地了解日常认知。本文提出了一种多模态框架，利用脑电图（EEG）、可穿戴生理信号、行为背景和自我报告测量来追踪人类认知活动。一项初步试点研究对三名参与者（N = 3）的观察数据进行了分析，这些数据涵盖两周内跨越九个活动领域的280个带注释的10分钟间隔。结果揭示了一致的时间模式，包括在13:00时动机和能量出现明显的午间下降，随后在下午恢复。工作和工具性日常生活活动（IADLs）产生了最高的心流状态率（分别为51%和50%），而基本日常生活活动（ADLs）产生的最低（15%）。动机与唤醒度（r = 0.78）和注意力（r = 0.74）强相关，而感知压力则表现出较弱的负相关关系（r = -0.33）。一个基于唤醒度、注意力、能量和压力预测动机的线性回归模型实现了R2 = 0.76（MAE = 9.84，RMSE = 12.85）。基于滞后的分析表明，先前的能量水平正向预测随后的动机，证实了认知动态中的时间依赖性。这些发现证明了在真实世界环境中进行多模态认知活动分析的可行性，并强调了整合生理和行为指标的重要性。所提出的框架为未来大规模多模态系统和应用智能解决方案奠定了基础。
英文摘要
Understanding human cognitive activity in everyday life remains challenging due to the dynamic, context-dependent, and multimodal nature of cognition. Laboratory-based studies often fail to capture real-world cognitive processes, while single-modality approaches provide only partial insight into cognitive states. Advances in wearable sensing now enable the collection of heterogeneous data streams for a more comprehensive view of daily cognition. This paper presents a multimodal framework for tracking human cognitive activity using electroencephalography (EEG), wearable physiological signals, behavioral context, and self-reported measures. A preliminary pilot study was conducted with observational data from three participants (N = 3) over 280 annotated 10-minute intervals spanning nine activity domains across two weeks. Results reveal consistent temporal patterns, including a discernible mid-day decrease in motivation and energy at 13:00, followed by afternoon recovery. Work and IADLs yielded the highest flow state rates (51% and 50%), while ADLs produced the lowest (15%). Motivation correlated strongly with arousal (r = 0.78) and attention (r = 0.74), whereas perceived stress showed a weaker negative relationship (r = -0.33). A linear regression model predicting motivation from arousal, attention, energy, and stress achieved R2 = 0.76 (MAE = 9.84, RMSE = 12.85). Lag-based analysis indicates that prior energy levels positively predict subsequent motivation, confirming temporal dependencies in cognitive dynamics. These findings demonstrate the feasibility of multimodal cognitive activity analysis in real-world environments and highlight the importance of integrating physiological and behavioral indicators. The proposed framework provides a foundation for future large-scale multimodal systems and applied intelligent solutions.
