Classification of motor states from brain rhythms using lattice neural networks

Abstract

The identification of each phase in the process of movement arms from brain waves has been studied using classical classification approaches. Identify precisely each movement phase from relaxation to movement execution itself, is still an open challenging task. In the context of Brain-Computer Interfaces (BCI) this identification could accurately activate devices, giving more natural control systems. This work presents the use of a novel classification technique Lattice Neural Networks with Dendritic Processing (LNNDP), to identify motor states using electroencephalographic signals recorded from healthy subjects, performing selfpaced reaching movements. To evaluate the performance of this technique 3 bi-classification scenarios were followed: (i) relax vs. intention, (ii) relax vs. execution, and (iii) intention vs. execution. The results showed that LNNDP provided an accuracy of (i) 65.26%, (ii) 69.07%, and (iii) 76.71% in each scenario respectively, which were higher than the chance level.