Decoding knee angle trajectory from electroencephalogram signal using NARX neural network and a new channel selection algorithm

Abstract

Objectives: The aim of this research was to reveal the electroencephalogram (EEG) signal changes to obtain the knee angle change trajectory during a movement. Approach: Initially, a number of recorded EEG channels were selected using a new proposed EEG channel selection method. The signals were recorded from 10 healthy subjects in two states of movement imagination and implementation. Then, a NARX (Nonlinear Autoregressive Exogenous) neural network estimated the motion pattern of knee angle using the selected channels of EEG data. Main results: The results indicated that movement information extracted from the selected channels in mu rhythm was more accurate. Significance: This research suggests an approach to design the desired motion trajectory of the knee joint using the information emerging from the motor control process.