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2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
ISSN: 2375-9356
ISBN: 978-1-4673-8795-8
pp: 427-429
Jinwon An , Department of Industrial Engineering, Seoul National University, Republic of Korea
Sungzoon Cho , Department of Industrial Engineering, Seoul National University, Republic of Korea
ABSTRACT
People who suffer from neuromuscular disorders and amputated limbs require prosthetic devices that are maneuvered through brain computer interfaces. Electroencephalography is a method to record the activity of the brain that is used for inputs for a brain computer interface. In this paper we propose a method for predicting hand motion phases in grasp-and-lift task from electroencephalography recordings using recurrent networks. Various architectures of recurrent neural networks are compared in terms of performance. For consistent prediction, moving average is applied.
INDEX TERMS
Electroencephalography, Recurrent neural networks, Prosthetics, Computer architecture, Feature extraction, Brain-computer interfaces, Backpropagation
CITATION

Jinwon An and Sungzoon Cho, "Hand motion identification of grasp-and-lift task from electroencephalography recordings using recurrent neural networks," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 427-429.
doi:10.1109/BIGCOMP.2016.7425963
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