EEG motor imagery applications in brain computer interface based wheelchair control

Nazre bin Abdul Rashid

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Type :	article
Subject :	QA76 Computer software
Main Author :	Nazre bin Abdul Rashid
Additional Authors :	M.S.Suzani Ziadoon Tareq Abdulwahhab Al-qaysi
Title :	EEG motor imagery applications in brain computer interface based wheelchair control

Place of Production :	Tanjong Malim
Publisher :	Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :	2019
Corporate Name :	Universiti Pendidikan Sultan Idris

Abstract : Universiti Pendidikan Sultan Idris

Principally, this study gives an overview about the Neuronal oscillations appear throughout the nervous system in structures as well as, the range of frequencies of clinical and physiological interests for motor imagery EEG signal. In addition, a brief description about MI paradigm which is also known as movement imagery, which is a mental process through which a person imagines a physical action, such as jumping, or moving hands. In particular, event-related desynchronization (ERD) and synchronization (ERS). Finally, a set of studies have been listed towards highlights the advantages of using BCI Competition dataset which is a public dataset that have been widely used in the analysis of the EEG motor imagery signal methods and technique.

References

[1] K. Koepsell, X. Wang, J. Hirsch, and F. T. Sommer, "Exploring the function of neural oscillations in early sensory systems," Frontiers in neuroscience, vol. 3, p. 10, 2010.

[2] M. A. Devi, R. Sharmila, and V. Saranya, "Hybrid brain computer interface in wheelchair using voice recognition sensors," in Computer Communication and Informatics (ICCCI), 2014 International Conference on, 2014, pp. 1-5.

[3] H. Adeli, Z. Zhou, and N. Dadmehr, "Analysis of EEG records in an epileptic patient using wavelet transform," Journal of neuroscience methods, vol. 123, pp. 69-87, 2003.

[4] C. R. Hema, M. Paulraj, S. Yaacob, A. Adom, and R. Nagarajan, "Single trial motor imagery classification for a four state brain machine interface," in Signal Processing & Its Applications, 2009. CSPA 2009. 5th International Colloquium on, 2009, pp. 39-41.

[5] K. Kaneswaran, K. Arshak, E. Burke, and J. Condron, "Towards a brain controlled assistive technology for powered mobility," in Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, 2010, pp. 4176-4180.

[6] L. Jiang, E. Tham, M. Yeo, Z. Wang, and B. Jiang, "Motor imagery controlled wheelchair system," in Industrial Electronics and Applications (ICIEA), 2014 IEEE 9th Conference on, 2014, pp. 532-535.

[7] A.-M. Cebolla, E. Palmero-Soler, A. Leroy, and G. Cheron, "EEG spectral generators involved in motor imagery: a swLORETA study," Frontiers in psychology, vol. 8, p. 2133, 2017.

[8] M. Carra and A. Balbinot, "Evaluation of sensorimotor rhythms to control a wheelchair," in Biosignals and Biorobotics Conference (BRC), 2013 ISSNIP, 2013, pp. 1-4.

[9] M. E. Abdalsalam, M. Z. Yusoff, N. Kamel, A. Malik, and M. Meselhy, "Mental task motor imagery classifications for noninvasive brain computer interface," in Intelligent and Advanced Systems (ICIAS), 2014 5th International Conference on, 2014, pp. 1-5.

[10] K. Choi and A. Cichocki, "Control of a wheelchair by motor imagery in real time," in International Conference on Intelligent Data Engineering and Automated Learning, 2008, pp. 330-337.

[11] A. Ferreira, D. C. Cavalieri, R. L. Silva, T. F. Bastos Filho, and M. Sarcinelli Filho, "A Versatile Robotic Wheelchair Commanded by Brain Signals or Eye Blinks," in BIODEVICES (2), 2008, pp. 62-67.

[12] J. Li, J. Liang, Q. Zhao, J. Li, K. Hong, and L. Zhang, "Design of assistive wheelchair system directly steered b human thoughts," International journal of neural systems, vol. 23, p. 1350013, 2013.

[13] J. Li, H. Ji, L. Cao, D. Zang, R. Gu, B. Xia, et al., "Evaluation and application of a hybrid brain computer interface for real wheelchair parallel control with multidegree of freedom," International journal of neural systems, vol. 24, p. 1450014, 2014.

[14] Y. Li, J. Pan, F. Wang, and Z. Yu, "A hybrid BCI system combining P300 and SSVEP and its application to wheelchair control," IEEE Transactions on Biomedical Engineering, vol. 60, pp. 3156-3166, 2013.

[15] J. d. R. Millán, F. Galán, D. Vanhooydonck, E. Lew, J. Philips, and M. Nuttin, "Asynchronous non-invasive brainactuated control of an intelligent wheelchair," in Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, 2009, pp. 3361-3364.

[16] J. Long, Y. Li, H. Wang, T. Yu, J. Pan, and F. Li, "A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 20, pp. 720-729, 2012.

[17] Á. Fernández-Rodríguez, F. Velasco-Álvarez, and R. RonAngevin, "Review of real brain-controlled wheelchairs," Journal of neural engineering, vol. 13, p. 061001, 2016.

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