Аңдатпа
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.