Mental workload is a critical factor in human-in-the-loop systems, directly influencing cognitive performance and decision making in high-stakes environments such as aviation, healthcare, and robotics. While electroencephalography (EEG) and electrooculography (EOG) offer promising physiological markers for workload estimation, existing devices are often bulky, restrictive, and susceptible to motion artifacts, limiting their practicality in real-world applications.
This study introduces a wireless, ultra-thin forehead e-tattoo that enables high-fidelity EEG and EOG monitoring while maintaining comfort, stability, and motion resistance. Unlike conventional systems, our e-tattoo leverages adhesive poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT: PSS) composite-coated electrodes to achieve low skin impedance, robust adhesion, and reliable signal acquisition during dynamic tasks. Integrated with a flexible printed circuit for on-device processing and wireless transmission, this system provides a minimally obtrusive and long-lasting solution for ambulatory cognitive monitoring. We validate the e-tattoo’s capability through a dual N-back mental workload task, correlating EEG/EOG signals with NASA Task Load Index (NASA-TLX) self-assessments and task performance. A machine-learning model trained on these physiological features successfully estimates mental workload across varying task difficulties, demonstrating the feasibility of real-time cognitive state decoding. Our work presents a breakthrough in wearable neurotechnology, offering a scalable, cost-effective, and user-friendly approach to continuous mental workload assessment. Future applications could include real-time cognitive load monitoring in pilots, operators, and healthcare professionals, advancing the field of human-machine interaction and personalized cognitive augmentation.

Highlights

A wireless e-tattoo enables stable EEG and EOG monitoring during tasks

APC-GPU electrodes enhance adhesion and reduce motion artifacts

Mental workload is decoded using forehead EEG and EOG features and machine learning

The ultra-thin design ensures comfort and compatibility with headgear

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