Detection of Cognitive Load Modulation by EDA and HRV

Area of Science:

• Physiology
• Neuroscience
• Human-Computer Interaction

Background:

• Autonomic nervous system activity, measured via electrodermal activity (EDA) and heart rate variability (HRV), provides insights into cognitive processes.
• Accurately quantifying cognitive load, a complex construct influenced by emotions, remains challenging using single biosignals.

Purpose of the Study:

• To investigate the combined utility of EDA and HRV markers for assessing cognitive load during mental tasks.
• To identify reliable biosignal-derived markers predictive of perceived cognitive load and mental workload.

Main Methods:

• Participants underwent a two-back task and viewed emotional images while EDA and HRV were recorded.
• HRV signals were analyzed using variable frequency complex demodulation (VFCDM) and wavelet packet transform (WPT).
• EDA indices were extracted using VFCDM (EDA_TVSYMP), WPT (EDA_WPT), and convex optimization (EDA_CVX).

Main Results:

• Significant differences in cognitive load and emotional states were observed, correlating with EDA_CVX, EDA_TVSYMP, and HF-HRV_VFCDM.
• EDA_TVSYMP and HF-HRV_VFCDM were identified as key predictors of the NASA-TLX cognitive load score.
• K-means clustering revealed three distinct profiles of autonomic responses based on EDA_TVSYMP and HF-HRV_VFCDM.

Conclusions:

• Combining EDA and HRV markers offers a more comprehensive and accurate assessment of perceived cognitive load than single biosignals.
• The identified EDA_TVSYMP and HF-HRV_VFCDM markers are valuable for monitoring mental workload in operators.
• Distinct autonomic response profiles highlight the complexity of cognitive load perception and the need for multi-modal biosignal analysis.