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Updated: Feb 26, 2026

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks
Published on: December 2, 2015
Wearable brain imaging with multimodal physiological monitoring
Gary E Strangman1,2,3, Vladimir Ivkovic1, Quan Zhang1,2
1Neural Systems Group, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts.
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.
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