Proceedings of the 10th EAI International Conference on Body Area Networks 2015
DOI: 10.4108/eai.28-9-2015.2261443
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Personalized neuroscience: User modeling of cognitive function and brain activity in the cloud

Abstract: Reliable detection and prediction of neural activity and behavior requires a user model of brain activity that dynamically adapts based on known time-dependent physiological processes, as well as unknown traits of the user. We have applied wireless electroencephalography (EEG) sensors, edge devices with feedback capability, and cloud-assisted data acquisition to realtime and longitudinal brain monitoring and alerting. Toward a user model of brain function, we collected neural and behavioral data from humans in… Show more

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Cited by 2 publications
(1 citation statement)
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“…The potential of SNNs for ubiquitous Internet of Things (IoT) and other applications can be fulfilled only if energyefficient and dedicated parallel hardware solutions are developed (Nandakumar et al, 2018). Wireless Electroencephalography (EEG) sensors, edge devices, cloud-assisted data capture, and longitudinal brain monitoring and alerting were used in brain science and technology (Nick et al, 2015). Cognitive processes about network interactions during creative performance have been identified and they are: Internally focused attention, prepotent-response inhibition, and goaldirected memory retrieval.…”
Section: Introductionmentioning
confidence: 99%
“…The potential of SNNs for ubiquitous Internet of Things (IoT) and other applications can be fulfilled only if energyefficient and dedicated parallel hardware solutions are developed (Nandakumar et al, 2018). Wireless Electroencephalography (EEG) sensors, edge devices, cloud-assisted data capture, and longitudinal brain monitoring and alerting were used in brain science and technology (Nick et al, 2015). Cognitive processes about network interactions during creative performance have been identified and they are: Internally focused attention, prepotent-response inhibition, and goaldirected memory retrieval.…”
Section: Introductionmentioning
confidence: 99%