SSVEP-EEG signal classification based on emotiv EPOC BCI and raspberry pi

Abstract:

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.

Año de publicación:

2021

Keywords:

• Brain computer interface
• Data acquisition
• classification
• Machine learning
• XGBoost
• SSVEP-EEG

Fuente:

scopus