Robots or 3D models control by Brain-Computer Interfaces

Author: Tomohiro Uchimura

Uchimura, Tomohiro, 2021 Robots or 3D models control by Brain-Computer Interfaces, Flinders University, College of Science and Engineering

Terms of Use: This electronic version is (or will be) made publicly available by Flinders University in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. You may use this material for uses permitted under the Copyright Act 1968. If you are the owner of any included third party copyright material and/or you believe that any material has been made available without permission of the copyright owner please contact with the details.


The brain emits electrical signals which is captured to produce electroencephalography (EEG) as representing human activity. These signals have been researched for connecting the brain and computer or machines. The brain-computer interface can collect EEG signals and send them to the computer. The technology is pretty helpful for disabled people to control products without their body movement. Moreover, BCI technology can expand the possibilities of the development of new products and entertainment.

Although many of these BCI devices and biosignal devices support the Lab streaming Layer as a reliable transfer protocol, the protocol is uncommon. Hence, implementing a brain control system on the products requires indepth knowledge of BCI technology and Lab streaming Layer. The project aims to develop convenient and flexible software (it is called the flexible software in the thesis) for connecting BCI devices and other products such as robots or 3D model control systems. The developed software can convert stimulations received from EEG devices into simple string commands and send these commands to control targets via standard transfer protocol, TCP. Combining the software and BCI devices and biosignal devices might reduce the barriers to entry for implementing brain control systems; it leads to boost technologys' evolution in lots of areas.

The system used OpenBCI hardware and software and OpenViBE software for collecting and analysing EEG signals. EEG headband and Ganglion were used as EEG equipment, and Sphero RVR Programmable Robot was chosen for a control target. As a demonstration of bio-signal collection and control, jaw clenching was used as a moving trigger to control Sphero RVR; when relaxing, the Sphero RVR stopped. The programming language was Python 3.5 was used to develop the flexible software. The system was evaluated from the point of view of accuracy and processing speed with two testers. Because having high accuracy and fast processing speed is essential for controlling system to avoid users' confusion, miss control, and accident. The conversion accuracy of jaw clenching marked 100% recognition; however, several relaxing states were missed. The processing speed of conversion and sending commands was pretty high (under 0.150 seconds). There were several limitations on the testing environments and some system delay problems; however, they can be resolved in future work. Hence, the software sufficiently meets the system requirements of the mediation software connecting BCI devices and other products. The project's success will help many manufacturers' brain control system implementation in lots of industries in the world.

Keywords: Brain-Computer Interface (BCI), Control system, Conversion Table, Jaw Clenching, Lab Streaming Layer (LSL), Motor Imagery, OpenBCI, OpenViBE, Robot, Sphero RVR, TCP

Subject: Computer Science thesis

Thesis type: Masters
Completed: 2021
School: College of Science and Engineering
Supervisor: Dr. Trent Lewis