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.
Highlights
Hybrid BCI systems have different types of signals, all put together to offer different degrees of solutions (Lin, Chen, Huang, Ding, & Gao, 2015); these include EEG, EMG and body movement, using relaxation and concentration, winking action and head movement respectively; these types of signals are active, where the user has control over them at any time without depending on an external stimulant.The purpose of the project is to develop a light installation and portable system where, as the main aspect, the Emotiv EPOC (Emotiv Systems, 2014) gives a straightforward result on the preparation by wearing the wireless system with a USB dongle
The EPOC delivers a wide range of magnitudes, from microvolts for the EEG waves to millivolts for the EMG aspects; it has a gyroscopic sensor of two axes especially for the head movement
The system needs sufficient memory and processing power to handle the features extraction and classification of multiple biological signals, the manipulation of the robot and a graphical interface, where the Raspberry Pi [RPi] (Upton, 2015) performs well enough with a focus on real-time implementation and no hint of any type of delay, which is ideal for a human–machine application
Summary
Hybrid BCI systems have different types of signals, all put together to offer different degrees of solutions (Lin, Chen, Huang, Ding, & Gao, 2015); these include EEG, EMG and body movement, using relaxation and concentration, winking action and head movement respectively; these types of signals are active, where the user has control over them at any time without depending on an external stimulant.The purpose of the project is to develop a light installation and portable system where, as the main aspect, the Emotiv EPOC (Emotiv Systems, 2014) gives a straightforward result on the preparation by wearing the wireless system with a USB dongle. The system needs sufficient memory and processing power to handle the features extraction and classification of multiple biological signals, the manipulation of the robot and a graphical interface, where the Raspberry Pi [RPi] (Upton, 2015) performs well enough with a focus on real-time implementation and no hint of any type of delay, which is ideal for a human–machine application. The programming language selected must be free, light and relatively new, and suitable to work with EPOC and RPi, but have a wide range of callable libraries; Python is an open language which offers different aspects for the processing of brain and muscular signals, from pre-processing, extraction of features such as power spectral density and Hjorth parameters, to classification [support vector machines] and execution of commands; it offers multiple functions related to the creation of a BCI
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