Evaluating the sensitivity of dry and gel-based wearable EEG for cognitive load estimation

Purpose:We present a large-scale (N= 120) comparative study of gel-based and dry electroencephalography (EEG) systems for cognitive load analysis in tasks involving information visualization (InfoVis) stimuli. Although dry systems are increasingly adopted owing to their portability and fast setup, their sensitivity to cognitive-related measurements (as compared to gel-based systems) remains debated. This limits the understanding of whether dry systems provide sufficient sensitivity for cognitive load assessment under controlled task conditions.Methods:We analyzed a diverse set of signal quality metrics, such as signal-to-noise ratio and channel retention, combined with spectral features across frequency bands to evaluate the ability for each device to capture workload-related neural markers during InfoVis tasks.Results:Although the gel-based device showed consistently better quality results than the dry one, the effect sizes suggest a small practical significance of the differences between systems. These results demonstrate that dry systems can provide adequate physiological sensitivity for cognitive load assessments.Conclusion:Our findings highlight the trade-off between usability (setup, calibration, etc) and data fidelity, providing practical guidance for choosing EEG systems for cognitive workload monitoring and applied neuroengineering research. Overall, the results suggest that dry systems can support coarse-grained cognitive load assessment, while gel-based systems remain advantageous when greater sensitivity is required.