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.

Object Affordance-Based Implicit Interaction for Wheelchair-Mounted Robotic Arm Using a Laser Pointer

“…In these studies, the interaction operations mapped the residual limb movement to robot instructions, such as forward, back, left, right, rotation, or other cartesian motions for the robotic arm, and some preset simple household tasks, which could help users perform some structured tasks, but still needing frequent limb movement. In addition, using biological signals remote control robots to perform the tasks would substantially reduce limb movements and physical burden [10][11][12][13]. However, the time-consuming signal recognition, complex operation, and high cost limit their development.…”

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 ].…”

EEG-controlled tele-grasping for undefined objects

“…For instance, workers doing physical tasks with both hands in the industrial workspace would need the capability of hands-free teleoperation to conduct additional tasks simultaneously (Liu et al, 2021;Škulj et al, 2021). In addition to industrial purposes, it can be used to assist patients suffering from motor disabilities in an upper limb due to stroke or spinal cord injury (Meng et al, 2016;Chen et al, 2018;Quiles et al, 2022;Zhou et al, 2023). As grasping is an essential task to perform activities of daily living (ADL) (Roy et al, 2017), teleoperation of robotic grasping without upper limb motion can help these patients in fulfilling basic skills in everyday life.…”

“…Several previous studies utilized the SSVEP-BCI to achieve EEG-controlled robotic arm systems and their grasping capabilities, as illustrated in Table 1. The subject could move the end-effector not only within a two-dimensional (2D) plane (Cao et al, 2021) but also in three-dimensional (3D) space (Chen et al, 2018;Zhu et al, 2020;Peng et al, 2022;Quiles et al, 2022;Zhou et al, 2023) by using six or more flickering blocks. The target object for grasping also could be selected by linking each flickering block to a placed object (Yang et al, 2017;Chen et al, 2019;Li and Kesavadas, 2022).…”