  Brain Computer Interfaces (BCI) provide the opportunity to control external
devices using the brain ElectroEncephaloGram (EEG) signals. In this paper we
propose two software framework in order to control a 5 degree of freedom
robotic and prosthetic hand. Results are presented where an Emotiv Cognitive
Suite (i.e. the 1st framework) combined with an embedded software system (i.e.
an open source Arduino board) is able to control the hand through character
input associated with the taught actions of the suite. This system provides
evidence of the feasibility of brain signals being a viable approach to
controlling the chosen prosthetic. Results are then presented in the second
framework. This latter one allowed for the training and classification of EEG
signals for motor imagery tasks. When analysing the system, clear visual
representations of the performance and accuracy are presented in the results
using a confusion matrix, accuracy measurement and a feedback bar signifying
signal strength. Experiments with various acquisition datasets were carried out
and with a critical evaluation of the results given. Finally depending on the
classification of the brain signal a Python script outputs the driving command
to the Arduino to control the prosthetic. The proposed architecture performs
overall good results for the design and implementation of economically
convenient BCI and prosthesis.
