| Resumen |
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 % and
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, 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. |
