A Machinery Arm System Controlled by EEG

Yan Zhao Wang; Gui Hao Liang; Jian Zhang

doi:10.4028/www.scientific.net/AMR.889-890.987

Paper Titles

Research on the Dynamic Model and ECU HIL Simulation System of Electronic-Controlled Engine
p.962

Research on Modeling and Parameter Identification of Electro-Hydraulic Servo Loading System
p.970

Difference Inequalities and Application to Discrete-Time Control Systems
p.978

An Intelligent Control System for LED Headlamp
p.982

A Machinery Arm System Controlled by EEG
p.987

Study on Analog Control Experimental Training Using PLC
p.994

Simulation and Experimental of Shifting Performance of Construction Machinery
p.998

Thermal Error Measurement, Modeling and Compensation for Motorized Spindle and the Research on Compensation Effect Validation
p.1003

A STEP Compliant Framework for Cutting Force Prediction for NC Turning Operations
p.1009

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 889-890A Machinery Arm System Controlled by EEG

A Machinery Arm System Controlled by EEG

Abstract:

The following description illustrates the implementation of an EEG-based Brain Computer Interface (BCI) for control of robotic arm utilizing electroencephalography (EEG) rhythms. The signals are collected with a headset witch denoises and classifies sampling signals adopting Fast Fourier Transform (FFT) and Kalman filter. These signals are deciphered to signals that can be eventually understood by certain machine, and then transmitted to a single-chip system by employing Bluetooth Module. Signals received by end-user machine will be compiled into control commands by MCU system and these commands are used to control actions of a robotic arm. Keywords: electroencephalogram;EEG;brain computer interface;BCI;MCU;robotic arm

You might also be interested in these eBooks

Engineering Solutions for Manufacturing Processes IV

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 889-890)

Pages:

987-993

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.889-890.987

Citation:

Cite this paper

Online since:

February 2014

Authors:

Yan Zhao Wang*, Gui Hao Liang, Jian Zhang

Keywords:

Brain Computer Interface (BCI), EEG, Electroencephalogram, MCU, Robotic Arm

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] J.R. Wolpaw,N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T.M. Vaughan: Braincomputer interfaces for communication and control, Clinical Neurophysiology, 113, vol. 6, 2002, pp.767-791.

DOI: 10.1016/s1388-2457(02)00057-3

Google Scholar

[2] Touradj Ebrahimi, Jean-Mare Vesin, Gayr Gareia , Brain-ComPuter Interafce in Multimedia Communieation, IEEE Signal Proeessing , 2003, 1: 14-24.

Google Scholar

[3] G. Pfurtscheller, C. Guger, G. Müller, G. Krausz, and C. Neuper, Brain oscillations control hand orthosis in a tetraplegic, , Neurosci. Lett., vol. 292, pp.211-214, (2000).

DOI: 10.1016/s0304-3940(00)01471-3

Google Scholar

[4] Wei bin, Jia cunliang: The Designs of EEG Signals Pre-processing Cuicuits, (2007).

Google Scholar

[5] D. M. Taylor, S. I. H. Tillery, & A. B. Schwartz, Direct Cortical Control of 3D Neuroprosthetic Devices, Science, vol. 296(5574), 2002, pp.1829-1832.

DOI: 10.1126/science.1070291

Google Scholar

[6] E. Margalit, J. D. Weiland, R. E. Clatterbuck, G. Y. Fujii, M. Maia, and M. Tameesh, Visual and electrical evoked response recorded from subdural electrodes implanted above the visual cortex in normal dogs under two methods of anesthesia, Journal of Neuroscience Methods, vol. 123(2), 2003, pp.129-137.

DOI: 10.1016/s0165-0270(02)00345-x

Google Scholar

[7] F. Wallois, A. Patil, C. Héberlé, and R. Grebe, EEG-NIRS in epilepsy in children and neonates, Neurophysiologie Clinique/Clinical Neurophysiology, vol. 40(5-6), 2010, pp.281-292.

DOI: 10.1016/j.neucli.2010.08.004

Google Scholar

[8] R. A. Andersen, S. Musallam, B. Pesaran, Selecting the signals for a brain-machine interface, Current Opinion in Neurobiology, vol. 14(6), 2004, pp.720-726.

DOI: 10.1016/j.conb.2004.10.005

Google Scholar

[9] E. C. Leuthardt, G. Schalk, J. R. Wolpaw, J. G. Ojemann, D. W. Moran, A brain-computer interface using electrocorticographic signals in humans, Journal of Neural Engineering, vol. 1(2), 2004, pp.63-71.

DOI: 10.1088/1741-2560/1/2/001

Google Scholar

[10] M. V. Gerven, J. Farquhar, R. Schaefer, R. Vlek, J. Geuze, A. Nijholt, N. Ramsey, P. Haselager, L. Vuurpijl, S. Gielen, and P. Desain, The brain-computer interface cycle, Journal of Neural Engineering, vol. 6(4), 2009, pp.041001-041010.

DOI: 10.1088/1741-2560/6/4/041001

Google Scholar

[11] Xing Song, S. Q. Xie, and K. C. Aw, EEG-Based Brain Computer Interface for Game Control, International Conference on Affective Computing and Intelligent Interaction, Lecture Notes in Information Technology, vol. 10, 2012, pp.47-54.

Google Scholar

[12] Information on http: /developer. neurosky. com.

Google Scholar

[13] Carlos de M. Cordeiro, Sachin Abhyankar, Rishi Toshiwal and Dharma P. Agrawal, BlueStar: Enabling Efficient Integration Between Bluetooth WPANs and IEEE 802. 11 WLANs, Mobile Networks and Applications, Springer, 2004-4.

DOI: 10.1023/b:mone.0000031607.96260.64

Google Scholar

[14] Qing Zhi Du, Yi Peng, Xiu Mei Yang and Qiu Ping Yang, The Research of CR's Application for Bluetooth Network, Advanced Materials Research, vol. 658, 2013, pp.574-577.

DOI: 10.4028/www.scientific.net/amr.658.574

Google Scholar