ENG  Vol.5 No.10 B , October 2013
Mean Threshold and ARNN Algorithms for Identification of Eye Commands in an EEG-Controlled Wheelchair
Abstract

This paper represented Autoregressive Neural Network (ARNN) and meant threshold methods for recognizing eye movements for control of an electrical wheelchair using EEG technology. The eye movements such as eyes open, eyes blinks, glancing left and glancing right related to a few areas of human brain were investigated. A Hamming low pass filter was applied to remove noise and artifacts of the eye signals and to extract the frequency range of the measured signals. An autoregressive model was employed to produce coefficients containing features of the EEG eye signals. The coefficients obtained were inserted the input layer of a neural network model to classify the eye activities. In addition, a mean threshold algorithm was employed for classifying eye movements. Two methods were compared to find the better one for applying in the wheelchair control to follow users to reach the desired direction. Experimental results of controlling the wheelchair in the indoor environment illustrated the effectiveness of the proposed approaches.


Cite this paper
Hai, N. , Trung, N. and Toi, V. (2013) Mean Threshold and ARNN Algorithms for Identification of Eye Commands in an EEG-Controlled Wheelchair. Engineering, 5, 284-291. doi: 10.4236/eng.2013.510B059.
References

[1]   J. Wolpaw, N. Birbaumer, D. McFarland, G. Pfurtschellere and T. Vaughan, “Brain-Computer Interfaces for Communication and Control,” Clinical Neuro-physiology, 2002, pp. 767-791. http://dx.doi.org/10.1016/S1388-2457(02)00057-3

[2]   N. Ince, F. Goksu, A. Tewfik and S. Arica, “Adapting Subject Specific Motor Imagery EEG Patterns in Space-Time-Frequency for a Brain Computer Interface,” Biomedical Signal Processing and Control, 2009, pp. 236- 246. http://dx.doi.org/10.1016/j.bspc.2009.03.005

[3]   N. Weiskopf, F. Scharnowski, R. Veit, R. Goebel, N. Birbaumer and K. Mathiak, “Self-Regulation of Local Brain Activity Using Real-Time Functional Magnetic Resonance Imaging (fMRI),” Journal of Physiology, 2004, pp. 357-373.

[4]   S. Lloyd-Fox, A. Blasi and ElwellCE, “Illuminating the Developing Brain: The Past, Present and Future of Functional Near Infrared Spectroscopy,” Neuroscience-Bio-behavioral, 2010, pp. 269-284.

[5]   G. E. Fabiani, D. J. McFarland, J. R. Wolpaw and G. Pfurtscheller, “Conversion of EEG Activity into Cursor Movement by a Brain-Computer Inter-face,” Transactions on Neural Systems and Rehabilita-tion Engineering, Vol. 12, pp. 331-338. http://dx.doi.org/10.1109/TNSRE.2004.834627

[6]   C. Guger, W. Harkam, C. Hertnaes and G. Pfurtscheller, “Prosthetic Control by an EEG-Based Brain-Computer Interface (BCI),” IEEE Transactions on Robotics, Vol. 21, 2005.

[7]   D. J. Krusienski and J. J. Shih, “A Case Study on the Relation between Electroencephalographic and Electrocorticographic Event-Related Potentials,” 32nd Annual International Conference of the IEEE EMBS, 2010.

[8]   D. J. McFarland and J. R. Wolpaw, “EEG-Based Communication and Control: Speed-Accuracy Relationships,” Applied Psychophysiology and Biofeedback, Vol. 28, 2003, pp. 217-231. http://dx.doi.org/10.1023/A:1024685214655

[9]   B. Blankertz, G. Dornhege, M. Krauledat, K.-R. Müller, V. Kunzmann, F. Losch and G. Curio, “The Berlin Brain-Computer Interface: EEG-Based Communication Without Subject Training,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 14, 2006, pp. 147- 152. http://dx.doi.org/10.1109/TNSRE.2006.875557

[10]   X. Gao, D. Xu, M. Cheng and S. Gao, “A BCI-Based Environmental Controller for the Motion-Disabled,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 11, 2003, pp. 137-140. http://dx.doi.org/10.1109/TNSRE.2003.814449

[11]   K. S. Ahmed, “Wheelchair Movement Control VIA Human Eye Blinks,” American Journal of Biomedical Engineering, Vol. 1, 2011, pp. 55-58.

[12]   T. Q. D. Khoa and M. Nakagawa, “Functional Near Infrared Spectroscope for Cognition Brain Tasks by Wavelets Analysis and Neural Networks,” International Journal of Biological and Life Sciences, Vol. 4, 2008, pp. 28-33.

[13]   R. Singla, B. Chambayil, A. Khosla and J. Santosh, “Comparison of SVM and ANN for Classification of Eye Events in EEG,” Journal of Biomedical Science and Engineering, Vol. 4, 2011, pp. 62-69.

[14]   S. Chabaa, A. Zeroual and J. Antari, “Identification and Prediction of Internet Traffic Using Artificial Neural Networks,” Intelligent Systems & Applications, Vol. 2, 2010, pp. 147-155. http://dx.doi.org/10.4236/jilsa.2010.23018

[15]   N.-J. Huan and R. Palaniappan, “Neural Network Classification of Autoregressive Features from Electroence-phalogram Signals for Brain-Computer Interface Design,” Journal of Neural Engineering, Vol. 1, 2004, pp. 142-150. http://dx.doi.org/10.1088/1741-2560/1/3/003

 
 
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