A Low Cost Eeg Based Bci Prosthetic Using Motor Imagery
Daniel Elstob, Emanuele Lindo Secco

TL;DR
This paper presents a low-cost EEG-based brain-computer interface system for controlling a prosthetic hand, combining software frameworks and motor imagery classification to achieve effective control with economical components.
Contribution
The paper introduces a novel low-cost BCI architecture integrating EEG signal classification with prosthetic control, demonstrating feasibility and performance improvements.
Findings
Successful control of prosthetic hand via EEG signals
High accuracy in motor imagery classification
Economical system design with open-source components
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…
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Taxonomy
TopicsEEG and Brain-Computer Interfaces · Neuroscience and Neural Engineering · Advanced Memory and Neural Computing
