Neurophysiology in the Wild

Borges, Daniel Filipe; Leal, Alberto

http://hdl.handle.net/10400.22/26272

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Authors

Borges, Daniel Filipe

Leal, Alberto

Abstract(s)

Introduction: The acquisition of bioelectrical signals from the nervous system is traditionally complex, prone to artifacts and technically demanding. Currently, there are signs that research in this field is saturated and confined to institutional walls due to the cost-effectiveness profile of the methods used. Therefore, the demand for portable technology has grown exponentially to significantly increase the amount of data collected. Therefore, epilepsy, specifically regarding long-term electroencephalography (EEG), and sleep medicine are two of the best examples of the clinical utility of wearables for real-world (Level IV) studies. Objective: Development of clinical and translational research based on the development, application and clinical validation of neurophysiological wearable technology to study neural networks in different clinical contexts. Methods: Using a tailor-made 2-channel EEG wearable device and a customized single-channel EEG sensor inserted into the external auditory canal, we conducted three clinical validation studies in patients' homes. We detected and sonified typical absence seizures in pediatric epilepsy patients, studied patients with clinically suspected temporal lobe epilepsy, and recorded and staged 30-second epochs from sleep studies of healthy and pathological subjects. Results: The wearable devices employed showed neurophysiological data with consistently strong positive correlations, consistent performance and high reliability compared to gold standard methods, as well as near perfect inter-observer agreement. Conclusions: Clinical neuroscientists require large amounts of data, so these developments, driven largely by the influx of gadgets in the consumer market, are enabling more do-it-yourself and quantified-self methods. These neurophysiological signals from wearables hold great promise for advancing neuroscience through real-world data.