Brain-computer interfaces in control of mechatronic devices and systems
DOI:
https://doi.org/10.34767/SIMIS.2018.02.01Keywords:
mechatronics, bioemdical engineering, brain-computer interface.Abstract
Brain-computer interfaces (BCIs) have begun to constitute the another breakthrough in contemporary neuroscience and neurorehabilitation. This paper provides an overview of brain-computer interfaces (BCIs) technology that aims to address the priorities for control of mechatronic devices and systems. We describe basic solutions in the area of BCIs and discuss technologies that may provide command signals for mechatronic devices. Despite continuous development of the topic there still remains room for improvement, including future interfaces and control signal classification enhancements.
References
Lobel D. A., Lee K. H. Brain Machine Interface and Limb Reanimation Technologies: Restoring Function After Spinal Cord Injury Through Development of a Bypass System, Mayo Clinic Proceedings, 2014; 89(5):708-714.
Augustyniak P., Przetwarzanie sygnałów elektrodiagnostycznych, Uczelniane Wydawnictwa Naukowo-Dydaktyczne AGH, Kraków,2001.
Kuniszyk-Jóźkowiak W., Przetwarzanie sygnałów biomedycznych, Wydawnictwo UMCS, Lublin 2011.
Lin W., Pierce A., Skalsky A. J., McDonald C. M., Mobility-assistive technology in progressive neuromuscular disease, Physical Medicine and Rehabilitation Clinics in North America, 2012; 23:885-894.
Dias M. S., Pires C. G., Pinto F. M., Teixeira V. D., Freitas J. Multimodal user interfaces to improve social integration of elderly and mobility impaired, Studies in Health Technology and Informatics, 2012; 177:14-25.
Akcakaya M., Peters B., Moghadamfalahi M., i in., Noninvasive brain-computer interfaces for augmentative and alternative communication. IEEE Reviews in Biomedical Engineering, 2014; 7:31-49.
Mikołajewska E., Mikołajewski D., Integrated IT environment of disabled people – a new concept, Central European Journal of Medicine, 2014; 9(1):177-182.
Mikołajewska E., Mikołajewski D., Wheelchairs development from the perspective of physical therapists and biomedical engineers. Advances in Clinical and Experimental Medicine, 2010; 19:771-776.
Mikołajewska E., Mikołajewski D., Exoskeletons in neurological diseases - current and potential future applications, Advances in Clinical and Experimental Medicine, 2011; 20:227–233.
Mikołajewska E., Mikołajewski D., E-learning in the education of people with disabilities, Advances in Clinical and Experimental Medicine, 2011; 20:103-109.
Mikołajewska E., Mikołajewski D. Neuroprostheses for increasing disabled patients' mobility and control, Advances in Clinical andExperimental Medicine, 2012; 21:263-272.
van den Brand, R., Heutschi, J., Barraud, Q., i in., Restoring voluntary control of locomotion after paralyzing spinal cord injury, Science, 2012; 336:1182-1185.
Dominici, N., Keller, U., Vallery, H., i in., Versatile robotic interface to evaluate, enable and train locomotion and balance after neuromotor disorders, Nature Medicine, 2012; 18:1142-1147.
Baranauskas G., What limits the performance of current invasive brain machine interfaces? Frontiers in Systems Neuroscience, 2014;8:68.
Khan M. J., Hong M. J., Hong K. S., Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface, Frontiers in Human Neuroscience, 2014; 8:244.
Lecuyer A., George L., Marchal M. Toward Adaptive VR Simulators Combining Visual, Haptic, and Brain-Computer Interfaces, IEEEComputer Graphics and Applications, 2013; 33(5):18-23.
Koo B., Lee H. G., Nam Y., i in. A Hybrid NIRSEEG System for Self-Paced Brain Computer Interface with Online Motor Imagery. Journal of Neuroscience Methods, 2015; 244:26-32.
