A Low Cost Eeg Based Bci Prosthetic Using Motor Imagery

Abstract

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.