A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent.

Skomrock, Nicholas D; Schwemmer, Michael A; Ting, Jordyn E; Trivedi, Hemang R; Sharma, Gaurav; Bockbrader, Marcia A; Friedenberg, David A

Skomrock, Nicholas D; Schwemmer, Michael A; Ting, Jordyn E; Trivedi, Hemang R; Sharma, Gaurav; Bockbrader, Marcia A; Friedenberg, David A.

Afiliación

Skomrock ND; Advanced Analytics and Health Research, Battelle Memorial Institute, Columbus, OH, United States.
Schwemmer MA; Advanced Analytics and Health Research, Battelle Memorial Institute, Columbus, OH, United States.
Ting JE; Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, United States.
Trivedi HR; Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, United States.
Sharma G; Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, United States.
Bockbrader MA; Neurological Institute, The Ohio State University, Columbus, OH, United States.
Friedenberg DA; Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, OH, United States.

Front Neurosci ; 12: 763, 2018.

Article en En | MEDLINE | ID: mdl-30459542

Palabras clave

brain-computer interface; decoding; deep learning; machine learning; response time; support vector machines

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Front Neurosci Año: 2018 Tipo del documento: Article País de afiliación: Estados Unidos

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