Weitere Kapitel dieses Buchs durch Wischen aufrufen
Feedback design is an important issue in motor imagery brain–computer interface (BCI) systems. However, extant research has not reported on the manner in which feedback presentation optimizes coadaptation between a human brain and motor imagery BCI systems. This study assesses the effect of realistic visual feedback on user BCI-performance and motor imagery skills. A previous study developed a teleoperation system for a pair of humanlike robotic hands and showed that the BCI control of the hands in conjunction with first-person perspective visual feedback of movements arouses a sense of embodiment in the operators. In the first stage of this study, the results indicated that the intensity of the ownership illusion was associated with feedback presentation and subject performance during BCI motion control. The second stage investigated the effect of positive and negative feedback bias on BCI-performance of subjects and motor imagery skills. The subject-specific classifier that was set up at the beginning of the experiment did not detect any significant changes in the online performance of subjects, and the evaluation of brain activity patterns revealed that the subject’s self-regulation of motor imagery features improved due to a positive feedback bias and the potential occurrence of ownership illusion. The findings suggest that the manipulation of feedback can generally play an important role with respect to training protocols for BCIs in the optimization of the subject’s motor imagery skills.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Alimardani, M., S. Nishio, and H. Ishiguro. 2013. Humanlike robot hands controlled by brain activity arouse illusion of ownership in operators. Science Reports 3. https://doi.org/10.1038/srep02396.
Alimardani, M., S. Nishio, and H. Ishiguro. 2014. Effect of biased feedback on motor imagery learning in BCI-teleoperation system. Frontiers in Systems Neuroscience 8: 52.
Armel, K.C., and V.S. Ramachandran. 2003. Projecting sensations to external objects: Evidence from skin conductance response. Proceedings of the Royal Society of London: Biological 270: 1499–1506. CrossRef
Barbero, Á., and M. Grosse-Wentrup. 2010. Biased feedback in brain-computer interfaces. Journal of Neuroengineering and Rehabilitation 7 (34): 1–4.
Curran, E.A., and M.J. Stokes. 2003. Learning to control brain activity: A review of the production and control of EEG components for driving brain–computer interface (BCI) systems. Brain and Cognition 51 (3): 326–336. CrossRef
Gonzalez-Franco, M., Y. Peng, Z. Dan, H. Bo, and G. Shangkai. 2011. Motor imagery based brain-computer interface: A study of the effect of positive and negative feedback. In Engineering in medicine and biology society, EMBC, Annual international conference of the IEEE.
Guger, C., H. Ramoser, and G. Pfurtscheller. 2000. Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI). IEEE Transactions on Rehabilitation Engineering 8 (4): 447–456. CrossRef
Lotte, F., F. Larrue, and C. Muhl. 2013. Flaws in current human training protocols for spontaneous brain-computer interfaces: Lessons learned from instructional design. Frontiers in Human Neuroscience 7 (568). https://doi.org/10.3389/fnhum.2013.00568.
Moore, D.S., and G.P. McCabe. 1998. Introduction to the practice of statistics, 3rd ed. New York: W. H. Freeman. MATH
Müller-Gerking, J., G. Pfurtscheller, and H. Flyvbjerg. 1999. Designing optimal spatial filters for single-trial EEG classification in a movement task. Clinical Neurophysiology 110: 787–798. CrossRef
Neuper, C., R. Scherer, S. Wriessnegger, and G. Pfurtscheller. 2009. Motor imagery and action observation: Modulation of sensorimotor brain rhythms during mental control of a brain–computer interface. Clinical Neurophysiology 120 (2): 239–247. CrossRef
Nishio, S., T. Watanabe, K. Ogawa, and H. Ishiguro. 2012. Body ownership transfer to teleoperated android robot. In International conference on social robotics, ICSR 2012, 398–407. CrossRef
Pfurtscheller, G., and C. Neuper. 2001. Motor imagery and direct brain-computer communication. Proceedings of the IEEE 89 (7): 1123–1134. CrossRef
- Adjusting Brain Activity with Body Ownership Transfer
- Springer Singapore
- Chapter 23
Neuer Inhalt/© Filograph | Getty Images | iStock