H. Huh, H. Shin, H. Li, N. Lu
The University of Texas at Austin,
United States
Keywords: e-tattoo, wearable device, forehead EEG, EOG, mental workload estimation
Summary:
Wearable technology for ambulatory human mental workload assessment enhances human-machine interaction by providing quantitative cognitive performance measures, especially when integrated with Internet of Things (IoT) or artificial intelligence (AI) technologies. However, challenges remain in developing sensors that can reliably detect mental workload in dynamic, real-world conditions. Traditional electroencephalography (EEG) and electrooculography (EOG) devices, with their dangling wires and time-consuming setups, are restricting or impossible to monitor operators' cognitive states while performing tasks in reality. Key issues include reducing motion artifacts, minimizing signal noise, improving skin contact stability, and optimizing device comfort for long-term wear. Additionally, achieving low power consumption, compact design, and efficient data transmission are critical to ensure seamless integration with existing IoT and AI systems. To address these challenges, we introduce a forehead wearable e-tattoo that integrates EEG and EOG sensors. This system uses disposable, large-area electrodes made from adhesive PEDOT composite-coated graphite-deposited polyurethane (APC-GPU) along with a reusable, small-footprint flexible printed circuit (FPC). Our results demonstrate that the APC-GPU electrodes achieve a low contact impedance of 2.1 kOhm and strong skin adhesion of 80 N/m, enabling stable, high-fidelity EEG/EOG measurements over 5 hours, even during dynamic activities such as walking and running. In addition, our FPC for data acquisition and wireless data transmission achieves a small footprint of 858.37 mm^2 and a total weight of 8.1 g, including a 150 mAh battery. The island-serpentine structural design increases the FPC's stretchability and flexibility, allowing it to accommodate various head movements and facial expressions. In comparison with a commercial gel-based EEG system, our e-tattoo shows comparable signal fidelity and neural spectra captured from the forehead. In a controlled study involving six subjects performing dual N-back tasks, the e-tattoo provided reliable physiological data, which was used to train a random forest-based workload estimation model. We believe our findings will contribute significantly to the advancement of wearable technology for continuous cognitive state monitoring.