Hand motion identification of grasp-and-lift task from electroencephalography recordings using recurrent neural networks

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.