Using Morphological-Linear Neural Network for Upper Limb Movement Intention Recognition from EEG Signals

This study aims to compare classical and Morphological-Linear Neural Network (MLNN) algorithms for the intention recognition to perform different movements from electroencephalographic (EEG) signals. Three classification models were implemented and assessed to decode EEG motor imagery signals: (i) Morphological-Linear Neural Network (MLNN) (ii) Support Vector Machine (SVM) and (iii) Multilayer perceptron (MLP). Real EEG signals recorded during robot-assisted rehabilitation therapy were used to evaluate the performance of the proposed algorithms in the classification of three classes (relax, movement intention A Int A and movement intention B Int B) using multi-CSP based features extracted from EEG signals. The results of a ten-fold cross validation show similar results in terms of classification accuracy for the SVM and MLNN models. However, the number of parameters used in each model varies considerably (the MLNN model use less parameters than the SVM). This study indicates potential application of MLNNs for decoding movement intentions and its use to develop more natural and intuitive robot assisted neurorehabilitation therapies.