Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm

Abstract

Robotics has been successfully applied in the design of collaborative robots for assistance to people with motor disabilities. However, man-machine interaction is difficult for those who suffer severe motor disabilities. The aim of this study was to test the feasibility of a low-cost robotic arm control system with an EEG-based brain-computer interface (BCI). The BCI system relays on the Steady State Visually Evoked Potentials (SSVEP) paradigm. A cross-platform application was obtained in C++. This C++ platform, together with the open-source software Openvibe was used to control a Stäubli robot arm model TX60. Communication between Openvibe and the robot was carried out through the Virtual Reality Peripheral Network (VRPN) protocol. EEG signals were acquired with the 8-channel Enobio amplifier from Neuroelectrics. For the processing of the EEG signals, Common Spatial Pattern (CSP) filters and a Linear Discriminant Analysis classifier (LDA) were used. Five healthy subjects tried the BCI. This work allowed the communication and integration of a well-known BCI development platform such as Openvibe with the specific control software of a robot arm such as Stäubli TX60 using the VRPN protocol. It can be concluded from this study that it is possible to control the robotic arm with an SSVEP-based BCI with a reduced number of dry electrodes to facilitate the use of the system.

Effects of Background Music on Mental Fatigue in Steady-State Visually Evoked Potential-Based BCIs

“…A brain–computer interface is a communication system between a human and a computer [ 1 ] that allows one to send information or commands from the human brain to the outside world without the need for peripheral neural and muscular activity. Brain–computer interfaces (BCIs) help users express thoughts and control external devices by monitoring their brain activity [ 2 , 3 ]. A variety of methods for monitoring brain activity can be used to acquire brain signals in BCI systems, including electroencephalography (EEG), functional near-infrared spectroscopy [ 4 , 5 , 6 ], functional magnetic resonance imaging [ 7 ], and magnetoencephalography [ 8 ].…”

Event-Related Potential-Based Brain–Computer Interface Using the Thai Vowels’ and Numerals’ Auditory Stimulus Pattern

“…VEP-based BCI is a high-performance BCI method [ 16 , 17 , 18 ]. The method has been widely implemented to enhance communication and control; its applications include BCI spellers based on transient VEP (or P300) [ 19 ] and steady-state VEP (SSVEP)-based BCI for machine control [ 20 , 21 ]. Moreover, a low-cost human–machine interface system can also support some patients via techniques such as eye-tracking [ 22 ].…”