Multimodal quantification of cognitive load using a printed wearable facial bio-potential system

Area of Science:

• Cognitive Neuroscience
• Human-Computer Interaction
• Wearable Technology

Background:

• Cognitive load significantly impacts learning and task performance.
• Objective measurement of cognitive load is challenging, especially in dynamic environments.
• Current methods rely on subjective reports or complex stationary equipment.

Purpose of the Study:

• To develop and validate a wireless wearable system for objective cognitive load measurement.
• To compare the effectiveness of various physiological signals (EEG, EMG, EOG) for assessing cognitive load.
• To demonstrate the system's utility in dynamic, real-world settings.

Main Methods:

• A wireless wearable system collected facial bio-potential markers (EMG, EOG, EEG).
• A validated task paradigm synchronized physiological signals with cognitive load induction.
• Machine learning models were used to estimate cognitive load from collected data.

Main Results:

• Facial muscle activity (EMG) and eye movements (EOG) showed significant potential for cognitive load assessment.
• The system demonstrated effectiveness in an outdoor pilot experiment.
• Combined physiological signals and machine learning provided robust cognitive load estimation.

Conclusions:

• Wireless wearable systems offer a practical approach to measuring cognitive load.
• EMG and EOG are promising indicators for objective cognitive load assessment in dynamic settings.
• This research contributes to developing non-invasive, real-world cognitive load monitoring.