A Brain–Computer Interface for Control of a Virtual Prosthetic Hand

Brain–computer interfaces (BCIs) have emerged as an option that allows better communication between humans and some technological devices. This article presents a BCI based on the steady-state visual evoked potentials (SSVEP) paradigm and low-cost hardware to control a virtual prototype of a robotic hand. A LED-based device is proposed as a visual stimulator, and the Open BCI Ultracortex Biosensing Headset is used to acquire the electroencephalographic (EEG) signals for the BCI. The processing and classification of the obtained signals are described. Classifiers based on artificial neural networks (ANNs) and support vector machines (SVMs) are compared, demonstrating that the classifiers based on SVM have superior performance to those based on ANN. The classified EEG signals are used to implement different movements in a virtual prosthetic hand using a co-simulation approach, showing the feasibility of BCI being implemented in the control of robotic hands.