Shredded control of drones via motor imagery brain computer interface
Keywords:
electroencephalogram, EEG, deep learning, drones, quadcopter, BCI, shredded controlAbstract
In this paper, we present architecture for an auxiliary shredded control that can be used in combination with a BCI control system. BCI systems are known for low reliability and accuracy. We are presenting a method to enhance this inherent weakness of BCI systems, by providing an assistive autonomous controller whenever the BCI system reaches to the point where fine control is necessary. The course control is performed by the motor imagery BCI which decodes the thinking process of the user and use it for navigating a quadcopter drone. Once the quadcopter reaches an area where the object selected to be picked up enters in the field of view of the bottom camera of the quadcopter, the autonomous controller is taking over the fine movement navigation to handle the rest of the task. This method is an exciting opportunity for several other BCI applications to enhance the reliability and accuracy of BCI systems to be adopted in everyday life of severely disabled patients.
References
P. Raina, S. Dukeshire, J. Lindsay, and L. W. Chambers, "Chronic conditions and disabilities among seniors: an analysis of populationbased health and activity limitation surveys," Annals of Epidemiology, vol. 8, no. 6, pp. 402-409, 1998.
H. Hoenig, D. H. Taylor Jr, and F. A. Sloan, "Does assistive technology substitute for personal assistance among the disabled elderly?," American journal of public health, vol. 93, no. 2, pp. 330-337, 2003.
B. Graimann, B. Z. Allison, and G. Pfurtscheller, Brain-computer interfaces: Revolutionizing human-computer interaction. Springer Science & Business Media, 2010.
V. Morash, O. Bai, S. Furlani, P. Lin, and M. Hallett, "Classifying EEG signals preceding right hand, left hand, tongue, and right foot
movements and motor imageries," Clinical neurophysiology, vol. 119, no. 11, pp. 2570-2578, 2008.
J. Wolpaw and E. W. Wolpaw, Brain-computer interfaces: principles and practice. OUP USA, 2012.
H. H. Alwasiti, I. Aris, and A. Jantan, "EEG activity in Muslim prayer: a pilot study," Maejo International Journal of Science and Technology, vol. 4, no. 3, pp. 496-511, 2010.
M. Mustafa, M. Taib, Z. Murat, and N. Sulaiman, "Comparison between KNN and ANN classification in brain balancing application via spectrogram image," Journal of Computer Science & Computational Mathematics, vol. 2, no. 4, pp. 17-22, 2012.
H. H. Alwasiti, I. Aris, and A. Jantan, "Brain computer interface design and applications: challenges and future," World Applied Sciences Journal, vol. 11, no. 7, pp. 819-825, 2010.
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, "Feature pyramid networks for object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2117-2125.
S. Ren, K. He, R. Girshick, and J. Sun, "Faster r-cnn: Towards realtime object detection with region proposal networks," in Advances in neural information processing systems, 2015, pp. 91-99.
A. L. Goldberger et al., "PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic
signals," circulation, vol. 101, no. 23, pp. e215-e220, 2000.
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