TY - GEN
T1 - Classification of hand movements from non-invasive brain signals using lattice neural networks with dendritic processing
AU - Ojeda, Leonardo
AU - Vega, Roberto
AU - Falcon, Luis Eduardo
AU - Sanchez-Ante, Gildardo
AU - Sossa, Humberto
AU - Antelis, Javier M.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Brain-Computer Interface
KW - Electroencephalogram
KW - Lattice Neural Network
KW - Motor imagery
UR - http://www.scopus.com/inward/record.url?scp=84937468482&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-19264-2_3
DO - 10.1007/978-3-319-19264-2_3
M3 - Contribución a la conferencia
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 23
EP - 32
BT - Pattern Recognition-7th Mexican Conference, MCPR 2015, Proceedings
A2 - Olvera López, José Arturo
A2 - Carrasco-Ochoa, Jesús Ariel
A2 - Martínez-Trinidad, José Francisco
A2 - Famili, Fazel
A2 - Sossa-Azuela, Juan Humberto
PB - Springer Verlag
T2 - 7th Mexican Conference on Pattern Recognition, MCPR 2015
Y2 - 24 June 2015 through 27 June 2015
ER -