Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jul 2;22(13):5000.
doi: 10.3390/s22135000.

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

Eduardo Quiles  1 , Javier Dadone  1 , Nayibe Chio  1   2 , Emilio García  1
Affiliations

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

Eduardo Quiles et al. Sensors (Basel). .

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.

Keywords: C++; Electroencephalography (EEG); Steady-State Visually Evoked Potential (SSVEP); brain computer interface (BCI); robot control.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Robot Stäubli TX60. (a) robot arm in the lab; (b) degrees of freedom scheme.
Figure 2
Figure 2
SSVEP-BCI methodology for robotic arm control.
Figure 3
Figure 3
Timing of a single SSVEP trial.
Figure 4
Figure 4
Time duration for starting on stimulation frequency and resting period in one run.
Figure 5
Figure 5
Stimuli frequencies.
Figure 6
Figure 6
Electrode disposition according to the international system 10–20.
Figure 7
Figure 7
Signal Processing Procedure.
Figure 8
Figure 8
GUI for the control of the robotic arm.
Figure 9
Figure 9
Control signal flowchart.
Figure 10
Figure 10
Combination of softkeys and their functionality.
Figure 11
Figure 11
Program execution threads.
Figure 12
Figure 12
Position of the targets and order of appearance.
Figure 13
Figure 13
Stäubli Robotic Suite environment showing the rotation of the extreme joint of the robot.
Figure 14
Figure 14
SSVEP BCI control of the Stäubli robotic arm.
Figure 15
Figure 15
Evolution of the time to complete the task in each attempt of the five subjects.
Figure 16
Figure 16
Average total time to complete task per subject.
Figure 17
Figure 17
Percentage of success of each subject for each attempt.
Figure 18
Figure 18
Distribution and average percentage of success of each subject.
Figure 19
Figure 19
Relationship between time and the number of total movements completed.
Figure 20
Figure 20
Distribution and average ITR of each subject.
See this image and copyright information in PMC

References

    1. Lotte F., Nam C.S., Nijholt A. Introduction: Evolution of Brain-Computer Interfaces. In: Nam C.S., Nijholt A., Lotte F., editors. Brain-Computer Interface Handbook: Technological and Theoretical Advances. CRC Press; Oxford, UK: 2018. pp. 1–8.
    1. Wolpaw J.R., Wolpaw E.W. Brain-Computer Interfaces: Something New under the Sun. Brain-Comput. Interfaces Princ. Pract. 2012;3:123–125. doi: 10.1093/acprof:oso/9780195388855.003.0001. - DOI
    1. Klein E., Nam C.S. Neuroethics and Brain-Computer Interfaces (BCIs) Brain-Comput. Interfaces. 2016;3:123–125. doi: 10.1080/2326263X.2016.1210989. - DOI
    1. Nijholt A., Nam C.S. Arts and Brain-Computer Interfaces (BCIs) Brain-Comput. Interfaces. 2015;2:57–59. doi: 10.1080/2326263X.2015.1100514. - DOI
    1. Schalk G., Brunner P., Gerhardt L.A., Bischof H., Wolpaw J.R. Brain-Computer Interfaces (BCIs): Detection Instead of Classification. J. Neurosci. Methods. 2008;167:51–62. doi: 10.1016/j.jneumeth.2007.08.010. - DOI - PubMed