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SSVEP-EEG Signal Classification based on Emotiv EPOC BCI and Raspberry Pi

2021; Elsevier BV; Volume: 54; Issue: 15 Linguagem: Inglês

10.1016/j.ifacol.2021.10.287

2405-8971

Víctor Asanza, Karla Avilés-Mendoza, Hector Trivino-Gonzalez, Félix Rosales-Uribe, Jamil Torres-Brunes, Francis Loayza, Enrique Peláez, Ricardo Cajo, Raquel Tinoco-Egas,

Neural dynamics and brain function

This work presents the experimental design for recording Electroencephalography (EEG) signals in 20 test subjects submitted to Steady-state visually evoked potential (SSVEP). The stimuli were performed with frequencies of 7, 9, 11 and 13 Hz. Furthermore, the implementation of a classification system based on SSVEP-EEG signals from the occipital region of the brain obtained with the Emotiv EPOC device is presented. These data were used to train algorithms based on artificial intelligence in a Raspberry Pi 4 Model B. Finally, this work demonstrates the possibility of classifying with times of up to 1.8 ms in embedded systems with low computational capacity.