EEG based motion intention prediction with neural networks

Abstract

EEG signal is recorded from the human scalp non-invasively and can be useful to control wearable robotic devices, according to the human motion intention. However, due to its high information density, the estimation of the user's motion intention from EEG signals is not easy. The user's motion intention might not be estimated when the user does not concentrate on the control of the robot and is distracted by other things. In this paper, a neural network based real-time estimation method is proposed to detect human motion intention using EEG signals. Results show that EEG signals can be successfully used to predict the 2-DOF motion intention of the user.