Gestural art: A Steady State Visual Evoked Potential (SSVEP) based Brain Computer Interface to express intentions through a robotic hand

Abstract

We present an automated solution for the acquisition, processing and classification of electroencephalography (EEG) signals in order to remotely control a remotely located robotic hand executing communicative gestures. The Brain-Computer Interface (BCI) was implemented using the Steady State Visual Evoked Potential (SSVEP) approach, a low-latency and low-noise method for reading multiple non-time-locked states from EEG signals. As EEG sensor, the low-cost commercial Emotiv EPOC headset was used to acquire signals from the parietal and occipital lobes. The data processing chain is implemented in OpenViBE, a dedicated software platform for designing, testing and applying Brain-Computer Interfaces. Recorded commands were communicated to an external server through a Virtual Reality Peripheral Network (VRPN) interface. During the training phase, the user controlled a local simulation of a dexterous robot hand, allowing for a safe environment in which to train. After training, the user's commands were used to remotely control a real dexterous robot hand located in Bologna (Italy) from Plymouth (UK). We report on the robustness, accuracy and latency of the setup.