TY - GEN
T1 - Development of a brain computer interface interface using multi-frequency visual stimulation and deep neural networks
AU - Perez-Benitez, J. L.
AU - Perez-Benitez, J. A.
AU - Espina-Hernandez, J. H.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/3/27
Y1 - 2018/3/27
N2 - This work proposes a Brain Computer Interface based on using multi-frequency visual stimulation and deep neural networks for signals classification. The use of multi-frequency stimulation, combined with a new proposed coding method codifying up to 220 commands, which could be used to create a large multi-command brain computer interface. The advantages this method for commands codification and classification performance is analyzed in a five commands Brain computer interface. The classification of the electroencephalographic signals used in the interface was performed using several algorithms. The outcomes reveal that the best classification algorithm is a deep neural network, which gives a classification accuracy of 97.78 %. This algorithm, also, allows establishing the most relevant features of the electroencephalographic signal spectrums for the classification and information extraction from the evoked potentials.
AB - This work proposes a Brain Computer Interface based on using multi-frequency visual stimulation and deep neural networks for signals classification. The use of multi-frequency stimulation, combined with a new proposed coding method codifying up to 220 commands, which could be used to create a large multi-command brain computer interface. The advantages this method for commands codification and classification performance is analyzed in a five commands Brain computer interface. The classification of the electroencephalographic signals used in the interface was performed using several algorithms. The outcomes reveal that the best classification algorithm is a deep neural network, which gives a classification accuracy of 97.78 %. This algorithm, also, allows establishing the most relevant features of the electroencephalographic signal spectrums for the classification and information extraction from the evoked potentials.
KW - Brain Computer Interface (BCI)
KW - Electroencephalography (EEG)
KW - Steady-state visual evoked potential (SSVEP)
KW - multi-frequency visual stimulation
UR - http://www.scopus.com/inward/record.url?scp=85047345151&partnerID=8YFLogxK
U2 - 10.1109/CONIELECOMP.2018.8327170
DO - 10.1109/CONIELECOMP.2018.8327170
M3 - Contribución a la conferencia
AN - SCOPUS:85047345151
T3 - 2018 28th International Conference on Electronics, Communications and Computers, CONIELECOMP 2018
SP - 18
EP - 24
BT - 2018 28th International Conference on Electronics, Communications and Computers, CONIELECOMP 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th International Conference on Electronics, Communications and Computers, CONIELECOMP 2018
Y2 - 21 February 2018 through 23 February 2018
ER -