Emotiv EPOC BCI with Python on a Raspberry pi

Authors

  • José Salgado Patrón Universidad Surcolombiana, Neiva
  • Cristian Raúl Barrera Monje Universidad Surcolombiana, Neiva

DOI:

https://doi.org/10.18046/syt.v14i36.2217

Keywords:

BCI, EEG, EPOC, Python, Raspberry Pi, support vector machine

Abstract

The hybrid Brain-Computer Interface [BCI] system gives an insight on the development of useful interfaces for users with different backgrounds, from medical applications to video games, where standalone and wearable means accessibility for the user. Systems such as EPOC offers a simple solution for acquiring electroencephalography and electromyography signals with low price and fast setup, compared to high tech medical equipment. From the processing point of view, a computer always offers the main foundation for solving any issue, as the Raspberry Pi [RPi] does, which provides the sufficient computational power for a BCI to be implemented and an open source operating system such as Raspbian. Certainly a wireless communication is a must between the robot and the RPi, where an Xbee module gives a simple bidirectional connection. Python is the principal tool used in the project with multiple libraries for the processing of brain and muscular signals not only for the preparation of them but classification as well, from multithreading functions, feature extraction such as power spectral density and Hjorth parameters, and a support vector machine  classifiera.

Author Biographies

  • José Salgado Patrón, Universidad Surcolombiana, Neiva
    Electronic Engineer; Master in Computing and Electronic Engineering; professor at the Universidad Surcolombiana (Neiva, Colombia): Electronic Engineering Program. His professional interest areas are: biomedical instrumentation, biomedical signal processing, robotics, and computational vision.
  • Cristian Raúl Barrera Monje, Universidad Surcolombiana, Neiva
    Student of Electronic Engineering at the Universidad Surcolombiana (Neiva, Colombia). His professional interest areas are: biomedical signal processing [EEG], learning machine, embedded systems and brain computer interfaces.

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Published

2016-03-30

Issue

Section

Original Research