Development of a brain computer interface interface using multi-frequency visual stimulation and deep neural networks

• Published in: 2018 International Conference on Electronics, Communications and Computers (CONIELECOMP) pp. 18 - 24
• Main Authors: Perez-Benitez, J.L., Perez-Benitez, J.A., Espina-Hernandez, J. H.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.02.2018
• Subjects: Brain Computer Interface (BCI); Brain-computer interfaces; Classification algorithms; Electroencephalography; Electroencephalography (EEG); Encoding; Frequency modulation; multi-frequency visual stimulation; Steady-state visual evoked potential (SSVEP); Time-frequency analysis; Visualization
• ISSN: 2474-9044
• DOI: 10.1109/CONIELECOMP.2018.8327170

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.