His primary areas of investigation include Electroencephalography, Epilepsy, Neuroscience, Artificial intelligence and Spike-timing-dependent plasticity. The study incorporates disciplines such as Ambulatory, Duration and Subclinical infection in addition to Electroencephalography. His Epilepsy research includes themes of Young adult, Anesthesia and Circadian rhythm.
His research on Neuroscience often connects related areas such as Neurotransmission. His Artificial intelligence study combines topics in areas such as Machine learning, Logistic regression, Pattern recognition and Autocorrelation. His Spike-timing-dependent plasticity research is multidisciplinary, incorporating elements of Nerve net and Premovement neuronal activity.
David B. Grayden mainly focuses on Neuroscience, Artificial intelligence, Epilepsy, Electroencephalography and Stimulation. The various areas that David B. Grayden examines in his Artificial intelligence study include Algorithm, Machine learning, Computer vision and Pattern recognition. His Circadian rhythm research extends to Epilepsy, which is thematically connected.
His work in the fields of Electroencephalography, such as Brain–computer interface and Electrocorticography, overlaps with other areas such as Population. His Artificial neural network research incorporates themes from Synaptic plasticity and Spike-timing-dependent plasticity. David B. Grayden has included themes like Speech recognition and Auditory perception in his Audiology study.
His scientific interests lie mostly in Epilepsy, Electroencephalography, Neuroscience, Brain–computer interface and Artificial intelligence. David B. Grayden interconnects Audiology and Circadian rhythm in the investigation of issues within Epilepsy. He carries out multidisciplinary research, doing studies in Electroencephalography and Term.
His work on Biomarker expands to the thematically related Neuroscience. His Brain–computer interface study incorporates themes from Speech recognition, Decoding methods, Convolutional neural network and Biomedical engineering. His Artificial intelligence research focuses on Machine learning and how it connects with Crowdsourcing.
David B. Grayden mainly investigates Epilepsy, Neuroscience, Electroencephalography, Stimulation and Audiology. His study in Epilepsy is interdisciplinary in nature, drawing from both Cohort and Circadian rhythm. His work in the fields of Neuroscience, such as Electrophysiology, Receptive field and Activating function, intersects with other areas such as Theoretical models.
His biological study spans a wide range of topics, including Crowdsourcing, Machine learning, Oscillation and Artificial intelligence. His work on Electrical brain stimulation as part of general Stimulation study is frequently linked to Deep brain stimulation, bridging the gap between disciplines. His Audiology study integrates concerns from other disciplines, such as Illusion, McGurk effect and Auditory perception.
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Speech Perception for Adults Who Use Hearing Aids in Conjunction With Cochlear Implants in Opposite Ears
Mansze Mok;David Grayden;Richard C. Dowell;David Lawrence.
Journal of Speech Language and Hearing Research (2006)
Interictal spikes and epileptic seizures: their relationship and underlying rhythmicity.
Philippa J. Karoly;Dean R. Freestone;Ray Boston;David B. Grayden.
Perceptual characterization of children with auditory neuropathy.
Gary Rance;Colette McKay;David Grayden.
Ear and Hearing (2004)
Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System.
Isabell Kiral-Kornek;Subhrajit Roy;Ewan Nurse;Ewan Nurse;Benjamin Mashford.
Minimally invasive endovascular stent-electrode array for high-fidelity, chronic recordings of cortical neural activity
Thomas J Oxley;Nicholas L Opie;Sam E John;Gil S Rind;Gil S Rind.
Nature Biotechnology (2016)
The circadian profile of epilepsy improves seizure forecasting.
Philippa J Karoly;Philippa J Karoly;Hoameng Ung;David B Grayden;Levin Kuhlmann;Levin Kuhlmann.
Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV: Structuring synaptic pathways among recurrent connections
Matthieu Gilson;Anthony N. Burkitt;David B. Grayden;Doreen A. Thomas.
Biological Cybernetics (2009)
Circadian and circaseptan rhythms in human epilepsy: a retrospective cohort study.
Philippa J Karoly;Daniel M Goldenholz;Daniel M Goldenholz;Dean R Freestone;Robert E Moss.
Lancet Neurology (2018)
Spike-timing-dependent plasticity: the relationship to rate-based learning for models with weight dynamics determined by a stable fixed point
Anthony N. Burkitt;Hamish Meffin;David B. Grayden.
Neural Computation (2004)
Epilepsyecosystem.org: crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG
Levin Kuhlmann;Levin Kuhlmann;Philippa Karoly;Dean R. Freestone;Benjamin H. Brinkmann.
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