A supplementary system for a brain-machine interface based on jaw artifacts for the bidimensional control of a robotic arm

• Published in: PloS one Vol. 9; no. 11; p. e112352
• Main Authors: Costa, Álvaro, Hortal, Enrique, Iáñez, Eduardo, Azorín, José M
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 12.11.2014; Public Library of Science (PLoS)
• Subjects: Adult; Algorithms; Architectural engineering; Biology and Life Sciences; Body mass; Brain; Brain - physiology; Brain research; Brain-Computer Interfaces; Cognitive tasks; Commands; Computer and Information Sciences; Data acquisition; Disabilities; Disabled people; EEG; Electrodes; Electroencephalography; Electromyography; EMG; Engineering; Engineering and Technology; Humans; Interfaces; International conferences; Jaw; Jaw - innervation; Jaw - physiology; Male; Man-machine interfaces; Man-Machine Systems; Noise; People with disabilities; Rehabilitation; Robot arms; Robotics - instrumentation; Signal processing; Stability; Studies; Training; Austria; Spain
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0112352

Non-invasive Brain-Machine Interfaces (BMIs) are being used more and more these days to design systems focused on helping people with motor disabilities. Spontaneous BMIs translate user's brain signals into commands to control devices. On these systems, by and large, 2 different mental tasks can be detected with enough accuracy. However, a large training time is required and the system needs to be adjusted on each session. This paper presents a supplementary system that employs BMI sensors, allowing the use of 2 systems (the BMI system and the supplementary system) with the same data acquisition device. This supplementary system is designed to control a robotic arm in two dimensions using electromyographical (EMG) signals extracted from the electroencephalographical (EEG) recordings. These signals are voluntarily produced by users clenching their jaws. EEG signals (with EMG contributions) were registered and analyzed to obtain the electrodes and the range of frequencies which provide the best classification results for 5 different clenching tasks. A training stage, based on the 2-dimensional control of a cursor, was designed and used by the volunteers to get used to this control. Afterwards, the control was extrapolated to a robotic arm in a 2-dimensional workspace. Although the training performed by volunteers requires 70 minutes, the final results suggest that in a shorter period of time (45 min), users should be able to control the robotic arm in 2 dimensions with their jaws. The designed system is compared with a similar 2-dimensional system based on spontaneous BMIs, and our system shows faster and more accurate performance. This is due to the nature of the control signals. Brain potentials are much more difficult to control than the electromyographical signals produced by jaw clenches. Additionally, the presented system also shows an improvement in the results compared with an electrooculographic system in a similar environment.