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

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.