A Wearable Brain-Computer Interface Instrument with Aug- Mented Reality-Based Interface for General Applications
Main Article Content
Abstract
In the project we are demonstrating the combined usage Augmented Reality(AR) and brain faced com- puter interface(BI) which can be used to control the robotic acurator by. This method is more simple and more user friendly. Here brainwave senor will work in its normal setting detecting alpha, beta, and gam- ma signals. These signals are decoded to detect eye movements. These are very limited on its own since the number of combinations possible to make higher and more complex task possible. Asa solution to this AR is integrated with the BCI application to make control interface more user friendly. This application can be used in many cases including many robotic and device controlling cases. Here we use BCI-AR to detect eye paralysis that can be archive by detecting eye lid movement of person by wearing head bend.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
R. T. Azuma, “A survey of augmented reality,” Presence: Teleoperators & Virtual Environments, vol. 6, no. 4, pp. 355–385, 1997. [CrossRef]
R. Azuma, Y. Baillot, R. Behringer, S. Feiner, S. Julier, and B. acIntyre, “Recent advances in aug- mented reality,” tech. rep., NAVAL RESEARCH LAB WASHINGTON DC, 2001. [CrossRef]
A. Alamri, J. Cha, and A. El Saddik, “AR-RE- HAB: An augmented reality framework for post- stroke-patient rehabilitation,” IEEE Transactions on Instrumentation and Measurement, vol. 59, no. 10, pp. 2554–2563, 2010. [CrossRef]
D. Chatzopoulos, C. Bermejo, Z. Huang, and P. Hui, “Mobile augmented reality survey: From where we are to where we go,” IEEE Access, vol. 5, pp. 6917–6950, 2017. [CrossRef]
M. Rüßmann, M. Lorenz, P. Gerbert, M. Wald- ner, J. Justus, P. Engel, and M. Harnisch, “Industry 4.0: The future of pro- ductivity and growth in manufacturing industries,” Boston Consulting Group, vol. 9, 2015.
M. Rüßmann, M. Lorenz, P. Gerbert, M. Wald- ner, J. Justus, P. Engel, and M. Harnisch, “Industry 4.0: The future of productivity and growth in manufacturing indus- tries.” https: //bcg.com/publications/2015/engineered_products_ project_business_ industry_4_future_productivity_growth_manufac- turing_industries.aspx, 2015.
N. Gavish, T. Gutiérrez, S. Webel, J. Rodríguez, M. Peveri, U.
Bockholt, and F. Tecchia, “Evaluating virtual reality and aug- mented
reality training for industrial maintenance and assembly tasks,”
Interactive Learning Environments, vol. 23, no. 6, pp. 778–798, 2015.
[CrossRef]
H.-C. Yan, J.-H. Zhou, and C. K. Pang, “Machin- ery degradation
inspection and maintenance using a cost-optimal non-fixed periodic
strategy,” IEEE Transactions on Instrumentation and Mea- surement,
vol. 65, no. 9, pp. 2067–2077, 2016. [CrossRef]
R. Palmarini, J. A. Erkoyuncu, R. Roy, and H. Torabmostaedi, “A
systematic review of augmented reality applications in maintenance,”
Robotics and Computer-Integrated Manufacturing, vol. 49, pp. 215–
, 2018. [CrossRef]
J. R. Wolpaw, N. Birbaumer, W. J. Heetderks, D. J. McFarland, P. H.
Peckham, G. Schalk, E. Donchin, L. A. Quatrano, C. J. Robinson, and
T. M. Vaughan, “Brain-computer interface tech- nology: a review of
the first international meeting,” IEEE transactions on rehabilitation
engineering, vol. 8, no. 2, pp. 164–173, 2000. [CrossRef]
G. Schalk, D. J. McFarland, T. Hinterberger, N. Birbaumer, and J. R.
Wolpaw, “BCI2000: a general-purpose brain-com- puter interface
(BCI) system,” IEEE Transactions on biomedical engineer- ing, vol.
, no. 6, pp. 1034–1043, 2004. [CrossRef]
M. Ahn and S. C. Jun, “Performance variation in motor imagery brain–