TY - GEN
T1 - Classification of motor states from brain rhythms using lattice neural networks
AU - Gudiño-Mendoza, Berenice
AU - Sossa, Humberto
AU - Sanchez-Ante, Gildardo
AU - Antelis, Javier M.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Brain-Computer Interface
KW - Electroencephalogram
KW - Lattice Neural Network
KW - Motor states
UR - http://www.scopus.com/inward/record.url?scp=84977490345&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-39393-3_30
DO - 10.1007/978-3-319-39393-3_30
M3 - Contribución a la conferencia
SN - 9783319393926
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 303
EP - 312
BT - Pattern Recognition - 8th Mexican Conference, MCPR 2016, Proceedings
A2 - Olvera-López, José Arturo
A2 - Martínez-Trinidad, José Francisco
A2 - Carrasco-Ochoa, Jesús Ariel
A2 - Ayala-Ramírez, Víctor
A2 - Jiang, Xiaoyi
PB - Springer Verlag
T2 - 8th Mexican Conference on Pattern Recognition, MCPR 2016
Y2 - 22 June 2016 through 25 June 2016
ER -