Seunghyeb Ban

Sleep is a critical physiological process essential for overall brain health. Recent research shows that the glymphatic system is a key player in facilitating the metabolic waste clearance. However, the continuous real-time monitoring of brain water dynamics during sleep remains substantially challenging. Here, we introduce a soft, wearable near-infrared spectroscopy (NIRS) system to detect brain water dynamics potentially linked to glymphatic activity. The device features an integration of multi-wavelength LEDs and photodetectors. The in vivo study with multiple human subjects captures the device's overnight sleep monitoring in a natural home environment, revealing continuous changes in brain water dynamics across different sleep stages. Our results support the link between sleep stage-dependent water dynamics and glymphatic activity. Spectral analysis identifies several physiological rhythms during sleep, including respiration, heart rate, and oscillations linked to slow-wave activity. These advancements significantly enhance the NIRS system's potential for a deeper understanding of brain health.

Background: Sleep quality declines with age and is a known contributor to multiple chronic health conditions, including Alzheimer disease. Emerging evidence suggests that certain electroencephalography (EEG) neural signatures measured during sleep may be predictive of cognitive decline in older adults. Sleep EEG signals are traditionally measured using bulky, rigid, and uncomfortable equipment in an unfamiliar laboratory setting, which can negatively impact sleep signals. Due to these limitations, sleep EEG data acquisition is typically limited to a single night. Objective: This study aimed to validate our recently developed portable, skin-like EEG monitoring patch for 7 nights in the home environment in a pilot sample of young and older adults by evaluating usability and acceptance, and replicating age-related differences in sleep architecture observed in the polysomnography literature. Methods: Eighteen young adults and 18 cognitively unimpaired older adults without sleep disorders were enrolled (data from 11 young adults and 12 older adults were included in the analyses) in a 7-night study during which they wore novel, gel-free, wireless, ultrathin, skin-conforming, sleep monitoring, fabric-based patches. These patches were self-applied to the forehead and face for optimal usability and comfort. The patches incorporate laser-cut mesh electrodes with low-profile electronics (including a rechargeable battery and amplifier) and transmit EEG signals to a participant-controlled, Bluetooth-enabled, tablet-based data acquisition app. An automated algorithm was used to stage sleep and assess microarchitecture features from the EEG commonly impacted for each participant. Averages across nights were computed for these sleep features for each participant. Results: Young and older adults reported that the sleep patch was easy to use and comfortable to wear. There was no loss of signal power over 7 nights of wear across participants (retained-data signal-to-noise ratio over the 7-d period: young adult, mean 20.69, SD 12.78, maximum 52.13, minimum 5.19; older adult, mean 22.10, SD 9.39, maximum 49.96, minimum 13.79). Most datasets not retained were lost due to poor reference electrode adhesion on the nose (75/101, 74% of lost datasets in young adults and 57/88, 65% in older adults). Trained sleep technologists verified that the retained datasets were of sufficient quality to be scored without difficulty. Expected age-group differences in sleep features were observed, including age-related reductions in stage N3 sleep (young adult, mean 18.55, SD 6.70; older adult, mean 10.40, SD 6.43; Mann-Whitney U =42.0; P =.01) and reduced sleep spindle density (young adult, mean 2.92, SD 2.24; older adult, mean 0.94, SD 1.33; Mann-Whitney U =45.0; P =.006). Conclusions: This study demonstrates that our novel, comfortable, wearable patch can reliably measure physiological sleep data over multiple nights at home in adults across the lifespan, thereby making multinight sleep assessment in cognitive aging studies and clinical research more accessible than traditional polysomnography. In future studies, the small, lightweight system, which is highly scalable, can be shipped inexpensively to participants’ homes, making this technology and research accessible to individuals who may have difficulty traveling or who are hesitant to travel to a laboratory or clinic.

Skin-based wearable devices have gained significant attention due to advancements in soft materials and thin-film technologies. Nevertheless, traditional wearable electronics often entail expensive and intricate manufacturing processes and rely on metal-based substrates that are susceptible to corrosion and lack flexibility. In response to these challenges, this paper has emerged with an alternative substrate for wearable electrodes due to its cost-effectiveness and scalability in manufacturing. Paper-based electrodes offer an attractive solution with their inherent properties of high breathability, flexibility, biocompatibility, and tunability. In this study, we introduce carbon nanotube-based paper composites (CPC) electrodes designed for the continuous detection of biopotential signals, such as electrooculography (EOG), electrocardiogram (ECG), and electroencephalogram (EEG). To prevent direct skin contact with carbon nanotubes, we apply various packaging materials, including polydimethylsiloxane (PDMS), Eco-flex, polyimide (PI), and polyurethane (PU). We conduct a comparative analysis of their signal-to-noise ratios in comparison to conventional gel electrodes. Our system demonstrates real-time biopotential monitoring for continuous health tracking, utilizing CPC in conjunction with a portable data acquisition system. The collected data are analyzed to provide accurate heart rates, respiratory rates, and heart rate variability metrics. Additionally, we explore the feasibility using CPC for sleep monitoring by collecting EEG signals.

Eye movements show primary responses that reflect humans’ voluntary intention and conscious selection. Because visual perception is one of the fundamental sensory interactions in the brain, eye movements contain critical information regarding physical/psychological health, perception, intention, and preference. With the advancement of wearable device technologies, the performance of monitoring eye tracking has been significantly improved. It also has led to myriad applications for assisting and augmenting human activities. Among them, electrooculograms, measured by skin-mounted electrodes, have been widely used to track eye motions accurately. In addition, eye trackers that detect reflected optical signals offer alternative ways without using wearable sensors. This paper outlines a systematic summary of the latest research on various materials, sensors, and integrated systems for monitoring eye movements and enabling human-machine interfaces. Specifically, we summarize recent developments in soft materials, biocompatible materials, manufacturing methods, sensor functions, systems’ performances, and their applications in eye tracking. Finally, we discuss the remaining challenges and suggest research directions for future studies.