Classification of hand movements from non-invasive brain signals using lattice neural networks with dendritic processing


Abstract:

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.

Año de publicación:

2015

Keywords:

  • Brain-Computer Interface
  • Motor imagery
  • Lattice Neural Network
  • Electroencephalogram

Fuente:

scopusscopus

Tipo de documento:

Conference Object

Estado:

Acceso restringido

Áreas de conocimiento:

  • Red neuronal artificial
  • Ciencias de la computación

Áreas temáticas:

  • Métodos informáticos especiales
  • Fisiología humana
  • Ingeniería y operaciones afines