Classification of Hand Movements from Non-invasive Brain Signals Using Lattice Neural Networks with Dendritic Processing

EEG-based BCIs rely on classification methods to recognize the brain patterns that encode user's intention. However, decoding accuracies have reached a plateau and therefore novel classification techniques should be evaluated. This paper proposes the use of Lattice Neural Networks with Dendritic Processing LNND for the classification of hand movements from electroencephalographic EEG signals. The performance of this technique was evaluated and compared with classical classifiers using EEG signals recorded form participants performing motor tasks. The result showed that LNND provides: i the higher decoding accuracies in experiments using one electrode $$DA=80\,\%$$DA=80% and $$DA=80\,\%$$DA=80% for classification of motor execution and motor imagery, respectively; ii distributions of decoding accuracies significantly different and higher than the chance level $$p<0.05$$p<0.05, Wilcoxon signed-rank test in experiments using one, two, four and six electrodes. These results shows that LNND could be a powerful technique for the recognition of motor tasks in BCIs.