Kinesthetic Motor Imagery Based Brain-Computer Interface for Power Wheelchair Manoeuvring
Keywords:Artificial Neural Network, Brain-Computer Interface, Kinesthetic Motor Imagery, Powered Wheelchair,
AbstractPatients who are suffering from diseases like motor neurone diseases (MND), or trauma such as spinal cord injury (SCI), and amputation is not able to move. This paper presents work on combining the power wheelchair designed to aid the movement of the disabled patient, and a Brain-Computer Interface can be used to replace conventional joystick so that it can be controlled without using hands. The brain signal emanated during Motor Imagery tasks can be converted into control signal for power wheelchair manoeuvring. In this research, five subjects are requested to perform six Kinesthetic Motor Imagery tasks, and Electroencephalography (EEG) signals are recorded. The elliptic filter was used to remove power line noise. Three features, namely Fractal dimension (FD), Mel-frequency Cepstral Coefficients (MFCCs) and a combined feature of FD with MFCCs were extracted and evaluated by using Multilayer Perceptron Neural Network (MLPNN). The Levenberg-Marquardt training algorithm is used to train the networks, and the classification result of the MLPNN using a combined feature of FD with MFCCs achieved an average accuracy of 91.7%. The developed model is tested and evaluated with the simulated virtual environment created by MATLAB graphical user interface (GUI). The result suggests that the combined feature of FD with MFCCs and MLPNN can be used to classify Motor Imagery signal for directional control of powered wheelchair.
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain–computer interfaces for communication and control. Clinical neurophysiology, 113(6), 767-791.
Fehr, L., Langbein, W. E., & Skaar, S. B. (2000). Adequacy of power wheelchair control interfaces for persons with severe disabilities: a clinical survey. Journal of rehabilitation research and development, 37(3), 353–360.
Kewate, P., & Suryawanshi, P. (2014). Brain Machine Interface Automation System: A Review. International Journal of Scientific and Technology, Research, 3(3), 64-7.
Anupama, H. S., Cauvery, N. K., & Lingaraju, G. M. (2012). Brain Computer Interface and its Types – A Study. International Journal of Advances in Engineering & Technology, 3(2), 739-746.
Yuksel, B. F., Donnerer, M., Tompkin, J., & Steed, A. (2011). Novel P300 BCI interfaces to directly select physical and virtual objects.
Jasper, H. H., Klem, G. H., Lüders, H. O., & Elger, C. (1999). The tentwenty electrode system of the International Federation. Electroencephalogr Clin Neurophysiol, 52(3), 371-375.
Hema, C. R., Paulraj, M. P., Yaacob, S., Adom, A. H., & Nagarajan, R. (2007). Motor imagery signal classification for a four state brain machine interface. World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 1(5), 1375-1380.
Sun, A., Fan, B., & Jia, C. (2011, December). Motor imagery EEGbased online control system for upper artificial limb. In Transportation, Mechanical, and Electrical Engineering (TMEE), 2011 International Conference on (pp. 1646-1649). IEEE.
Avinash T., & Nandini J. (2015). Classification of Artefacts in EEG Signal Recordings and Overview of Removing Techniques. IJCA Proceedings on International Conference on Computer Technology, (7):46-50.
Testa, A., Gallo, D., & Langella, R. (2004). On the Processing of harmonics and interharmonics: using Hanning window in standard framework. IEEE Transactions on Power Delivery, 19(1), 28-34.
Paulraj, M. P., Yaccob, S. B., Hamid, A., Adom, B., Subramaniam, K., & Hema, C. R. (2012, December). EEG based hearing threshold classification using fractal feature and neural network. In Research and Development (SCOReD), 2012 IEEE Student Conference on (pp. 38- 41). IEEE.
Paulraj, M. P., Yaccob, S. B., & Yogesh, C. K. (2014, December). Fractal feature based detection of muscular and ocular artifacts in EEG signals. In Biomedical Engineering and Sciences (IECBES), 2014 IEEE Conference on (pp. 916-921). IEEE.
Phothisonothai, M., & Nakagawa, M. (2005). EEG-based fractal analysis of different motor imagery tasks using critical exponent method. International Journal of Biological and Life Sciences, 1(3), 175-180.
Wang, Q., Sourina, O., & Nguyen, M. K. (2011). Fractal dimension based neurofeedback in serious games. The Visual Computer, 27(4), 299-309.
Kesić, S., & Spasić, S. Z. (2016). Application of Higuchi's fractal dimension from basic to clinical neurophysiology: A review. Computer Methods and Programs in Biomedicine, 133, 55-70.
Muda, L., Begam, M., & Elamvazuthi, I. (2010). Voice recognition algorithms using mel frequency cepstral coefficient (MFCC) and dynamic time warping (DTW) techniques. arXiv preprint arXiv:1003.4083.
Abdul, W., Wong, J.W., “Cortical activities pattern recognition for the limbs motor action,” Intelligent Environments, 2008 IET 4th International Conference, pp. 1-7, 21-22 July 2008.
Othman, M., Wahab, A., & Khosrowabadi, R. (2009). MFCC for robust emotion detection using EEG. In Communications (MICC), 2009 IEEE 9th Malaysia International Conference on (pp. 98-101). IEEE.
Nguyen, P., Tran, D., Huang, X., & Sharma, D. (2012, January). A proposed feature extraction method for eeg-based person identification. In Proceedings on the International Conference on Artificial Intelligence (ICAI) (p. 1). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
Poh, H. L. (1991). A neural network approach for marketing strategies research and decision support. (Doctoral dissertation, Stanford University). Retrieved from http://www.worldcat.org/title/neuralnetwork-approach-for-marketing-strategies-research-and-decisionsupport/oclc/29572273.
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