Kai J. Miller focuses on Neuroscience, Brain–computer interface, Electrocorticography, Electroencephalography and Artificial intelligence. In the field of Neuroscience, his study on Premovement neuronal activity and Gating overlaps with subjects such as Automatic gain control and Electronic circuit. His Brain–computer interface study combines topics from a wide range of disciplines, such as Speech recognition, Decoding methods and Human–computer interaction.
His Electrocorticography study incorporates themes from Motor imagery, Motor skill and Motor learning. His research in Electroencephalography intersects with topics in Cerebral cortex, Statistical physics, Power law and Brain mapping. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Algorithm, Measure and Linear model.
The scientist’s investigation covers issues in Neuroscience, Electrocorticography, Electroencephalography, Brain–computer interface and Artificial intelligence. In general Neuroscience, his work in Motor cortex, Electrophysiology, Local field potential and Cortex is often linked to Deep brain stimulation linking many areas of study. He combines subjects such as Neurophysiology, Temporal cortex, Stimulus, Visual perception and Functional magnetic resonance imaging with his study of Electrocorticography.
His studies deal with areas such as Cerebral cortex, Visual cortex and Brain mapping as well as Electroencephalography. His Brain–computer interface research incorporates themes from Cursor, Speech recognition, Human–computer interaction and Motor learning. In his study, Visualization is strongly linked to Computer vision, which falls under the umbrella field of Artificial intelligence.
His primary areas of investigation include Neuroscience, Electrocorticography, Deep brain stimulation, Brain–computer interface and Electrophysiology. His Frequency domain research extends to Neuroscience, which is thematically connected. His Electrocorticography research focuses on Neurophysiology and how it relates to Time domain and Cortical network.
His study in the fields of Subthalamic nucleus under the domain of Deep brain stimulation overlaps with other disciplines such as Essential tremor, Taste and Anticipation. In his research on the topic of Brain–computer interface, Computer vision, Pattern recognition and Neural decoding is strongly related with Artificial intelligence. His Electrophysiology research includes themes of Metadata, Data mining, Temporal resolution and Audiology.
Deep brain stimulation, Neuroscience, Electrophysiology, Brain–computer interface and Intervention are his primary areas of study. In his papers, he integrates diverse fields, such as Neuroscience and Amyotrophic lateral sclerosis. His work deals with themes such as Metadata, Data mining, Temporal resolution, Intracranial Electroencephalography and Neuroimaging, which intersect with Electrophysiology.
His Brain–computer interface study integrates concerns from other disciplines, such as Electrocorticography, Feature and Perception. His work carried out in the field of Electrocorticography brings together such families of science as Human–computer interaction, State and Exoskeleton. His Epilepsy research includes elements of Transcranial magnetic stimulation, Neuromodulation, Neurostimulation, Transcranial direct-current stimulation and Pediatrics.
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Spectral changes in cortical surface potentials during motor movement.
Kai J. Miller;Eric C. Leuthardt;Gerwin Schalk;Rajesh P. N. Rao.
The Journal of Neuroscience (2007)
Review of the BCI Competition IV
Michael Tangermann;Klaus Robert Müller;Ad Aertsen;Niels Birbaumer.
Frontiers in Neuroscience (2012)
Two-dimensional movement control using electrocorticographic signals in humans
G. Schalk;G. Schalk;G. Schalk;K. J. Miller;N. R. Anderson;J. A. Wilson.
Journal of Neural Engineering (2008)
Decoding two-dimensional movement trajectories using electrocorticographic signals in humans.
G Schalk;J Kubánek;K J Miller;N R Anderson.
Journal of Neural Engineering (2007)
Power-Law Scaling in the Brain Surface Electric Potential
Kai J. Miller;Larry B. Sorensen;Jeffrey G. Ojemann;Marcel den Nijs.
PLOS Computational Biology (2009)
Cortical activity during motor execution, motor imagery, and imagery-based online feedback
Kai J. Miller;Gerwin Schalk;Eberhard E. Fetz;Marcel den Nijs.
Proceedings of the National Academy of Sciences of the United States of America (2010)
Exaggerated phase–amplitude coupling in the primary motor cortex in Parkinson disease
Coralie de Hemptinne;Elena S. Ryapolova-Webb;Ellen L. Air;Paul A. Garcia.
Proceedings of the National Academy of Sciences of the United States of America (2013)
Decoupling the cortical power spectrum reveals real-time representation of individual finger movements in humans.
K. J. Miller;S. Zanos;E. E. Fetz;M. den Nijs.
The Journal of Neuroscience (2009)
Online Electromyographic Control of a Robotic Prosthesis
P. Shenoy;K.J. Miller;B. Crawford;R.P.N. Rao.
IEEE Transactions on Biomedical Engineering (2008)
Electrocorticography-based brain computer Interface-the seattle experience
E.C. Leuthardt;K.J. Miller;G. Schalk;R.P.N. Rao.
international conference of the ieee engineering in medicine and biology society (2006)
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