Wearable brain imaging with multimodal physiological monitoring

Abstract

The brain is a central component of cognitive and physical human performance. Measures, including functional brain activation, cerebral perfusion, cerebral oxygenation, evoked electrical responses, and resting hemodynamic and electrical activity are all related to, or can predict, health status or performance decrements. However, measuring brain physiology typically requires large, stationary machines that are not suitable for mobile or self-monitoring. Moreover, when individuals are ambulatory, systemic physiological fluctuations-e.g., in heart rate, blood pressure, skin perfusion, and more-can interfere with noninvasive brain measurements. In efforts to address the physiological monitoring and performance assessment needs for astronauts during spaceflight, we have developed easy-to-use, wearable prototypes, such as NINscan, for near-infrared scanning, which can collect synchronized multimodal physiology data, including hemodynamic deep-tissue imaging (including brain and muscles), electroencephalography, electrocardiography, electromyography, electrooculography, accelerometry, gyroscopy, pressure, respiration, and temperature measurements. Given their self-contained and portable nature, these devices can be deployed in a much broader range of settings-including austere environments-thereby, enabling a wider range of novel medical and research physiology applications. We review these, including high-altitude assessments, self-deployable multimodal e.g., (polysomnographic) recordings in remote or low-resource environments, fluid shifts in variable-gravity, or spaceflight analog environments, intracranial brain motion during high-impact sports, and long-duration monitoring for clinical symptom-capture in various clinical conditions. In addition to further enhancing sensitivity and miniaturization, advanced computational algorithms could help support real-time feedback and alerts regarding performance and health.

Artificial intelligence in prediction of mental health disorders induced by the COVID-19 pandemic among health care workers

“…This phase ( Figure 3 ) is related to the acquisition of multimodal neuro-physiological reactions on stimulation paradigms proposed in the previous phase and computation of corresponding features relevant for prediction of mental health disorders. The proposed methodology is based on state-of-the-art sensors for measurements of the individual’s multimodal neuro-psycho-physiological reactions: functional near-infrared spectroscopy (fNIRS); electroencephalography (EEG); peripheral physiology, ie, electrocardiography (ECG), electromyography (EMG), electrodermal activity (EDA), respiration; speech/acoustic and linguistic reactions; and facial/gesture and oculomotor reactions ( 54 , 55 ). Such measurements, obtained as a response to relevant stimuli described in Phase 3, have the potential to objectivize traditional diagnostic methodology in psychiatry.…”

A Mini-Review on Functional Near-Infrared Spectroscopy (fNIRS): Where Do We Stand, and Where Should We Go?

“…The sector of wearable health technology is gaining endless interest. The use of low-cost wearable monitoring devices or wearable biosensors that allow for the constant monitoring of physiological signals, such as fNIRS signals, is essential for the advancement of both the diagnosis and treatment of diseases, as well as for monitoring active life styles [32,107].…”

Military applications of soldier physiological monitoring

“…The value of near infrared spectroscopy forehead measurements, perhaps in conjunction with single channel EEG, may add to alertness predictions. 38 Measurement of sleep history has been used to predict alertness status, especially when combined with the variation in alertness expected through the circadian cycle in a two process sleep and performance model. 39 General conclusions can be drawn about decreased performance and attention at various levels of sleep restriction or hours of sleep deprivation but many moderating factors from genetics of sleep resilience to the effect of naps and caffeine confound reliable performance predictions.…”