Engineering a Cognitive Load Assessment System Through Multimodal Sensor Fusion

Accurate quantification of cognitive load is essential for optimizing human-computer interaction systems. Methods: This study recruited 159 healthy participants and employed a hierarchical n-back task paradigm (0-back to 3-back) to induce graded levels of cognitive load. Multimodal physiological signals, including electroencephalogram (EEG), heart rate variability (HRV), and electrodermal activity (EDA), were recorded simultaneously to construct a cognitive load dataset encompassing three modalities. Temporal and frequency domain features were extracted from EEG signals, temporal and frequency domain parameters from HRV signals, and phase-amplitude integration and SCR frequency from EDA signals. A Kruskal-Wallis test was used to analyze significant differences in physiological indices across different cognitive load levels. Finally, a multiple linear regression model was employed to quantify the contribution of each modality's features to cognitive load classification. Results: (1) A significant suppression of alpha band power in the eyes-open resting state validated the effectiveness of the EEG signal acquisition system; (2) With increasing task difficulty, the alpha and theta power of EEG and the LF value of HRV showed significant monotonic increasing trends (p < 0.05), confirming the sensitivity of multimodal physiological signals to changes in cognitive load; (3) The regression model revealed that EEG features had the highest contribution (β = 0.57). Conclusion: This study proposes a framework for cognitive load quantification based on multimodal feature fusion, providing a theoretical and empirical foundation for the development of high-precision cognitive load assessment models.