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Real-Time BCI System Design to Control Arduino Based Speed Controllable Robot Using EEG pp 25–56Cite as

A Review on Algorithms for EEG-Based BCIs

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Abstract

Many classes of electrophysiological activities of the brain are used in designing various types of brain–computer interfaces (BCIs). Those are discussed briefly in this section. Sensorimotor activity generally corresponds to the behavior of the brain rhythms (mu, beta, and gamma), movement-related potentials (MRPs), etc. Next, the classification of BCI based on various parameters has also been discussed. These parameters are the mode of signal acquisition, timing, and placement of sensors. Later in this section, algorithms that have been and with chances of being, used in BCI applications have been discussed in a detailed manner. The algorithms chosen for each stage of the signal processing have an equal role to play in resulting a better outcome. Therefore, the section emphasizes the algorithms for each such stage, separately. Choosing a perfect algorithm is very important to design an efficient classifier. This section provides important information about the algorithms concerned with BCI.

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References

  • Ahmadi, M., and A. Erfanian. 2009 April. An on-line BCI system for hand movement control using real-time recurrent probabilistic neural network. In 4th International IEEE/EMBS Conference on Neural Engineering, 2009, NER’09, 367–370). New York: IEEE.

    Google Scholar 

  • Aimone, C.A., A.S. Garten, S.E. Grant, O. Mayrand, T. Zimmermann, and Interaxon Inc. 2014. Brain sensing headband. U.S. Patent D709,673.

    Google Scholar 

  • Aliki, M., and V. Emmanouel. 2008. Polysomnography: Recent Data on Procedure and Analysis. http://www.pneumon.org/assets/files/Archive/PNEUMON_2008-4.pdf#page=44.

  • Bashashati, A., M. Fatourechi, R.K. Ward, and G.E. Birch. 2007. A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals. Journal of Neural Engineering 4 (2): R32.

    Article  Google Scholar 

  • Birbaumer, N., A. Kubler, N. Ghanayim, T. Hinterberger, J. Perelmouter, J. Kaiser, I. Iversen, B. Kotchoubey, N. Neumann, and H. Flor. 2000. The thought translation device (TTD) for completely paralyzed patients. IEEE Transactions on Rehabilitation Engineering 8 (2): 190–193.

    Article  Google Scholar 

  • Cheemalapati, S., M. Gubanov, M. Del Vale, and A. Pyayt. 2013, August. A real-time classification algorithm for emotion detection using portable EEG. In 2013 IEEE 14th International Conference on Information Reuse and Integration (IRI), 720–723. New York: IEEE.

    Google Scholar 

  • Cincotti, F., D. Mattia, C. Babiloni, F. Carducci, S. Salinari, L. Bianchi, M.G. Marciani, and F. Babiloni. 2003. The use of EEG modifications due to motor imagery for brain-computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering 11 (2): 131–133.

    Article  Google Scholar 

  • Darvishi, S., and A. Al-Ani. 2007, August. Brain-computer interface analysis using continuous wavelet transform and adaptive neuro-fuzzy classifier. In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, 3220–3223. New York: IEEE.

    Google Scholar 

  • Dobrea, D.M., and M.C. Dobrea. 2009, November. Optimisation of a BCI system using the GA tehnique. In 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies, 2009. ISABEL 2009, 1–6. New York: IEEE.

    Google Scholar 

  • Emotiv, S.D.K. 2010. Research Edition Specifications.

    Google Scholar 

  • Girouard, A., E.T. Solovey, and R.J. Jacob. 2013. Designing a passive brain computer interface using real time classification of functional near-infrared spectroscopy. International Journal of Autonomous and Adaptive Communications Systems 6 (1): 26–44.

    Article  Google Scholar 

  • Graimann, B., B. Allison, and G. Pfurtscheller. 2009. Brain–computer interfaces: a gentle introduction. In Brain-Computer Interfaces, 1–27. Berlin: Springer.

    Google Scholar 

  • Kennedy, P.R., R.A. Bakay, M.M. Moore, K. Adams, and J. Goldwaithe. 2000. Direct control of a computer from the human central nervous system. IEEE Transactions on Rehabilitation Engineering 8 (2): 198–202.

    Article  Google Scholar 

  • Khorshidtalab, A., and M.J.E. Salami. 2011, May. EEG signal classification for real-time brain-computer interface applications: a review. In 2011 4th International Conference on Mechatronics (ICOM), 1–7. New York: IEEE.

    Google Scholar 

  • Kim, Y., N.L. Doh, Y. Youm, and W.K. Chung. 2007. Robust discrimination method of the electrooculogram signals for human-computer interaction controlling mobile robot. Intelligent Automation & Soft Computing 13 (3): 319–336.

    Article  Google Scholar 

  • Lalor, E.C., S.P. Kelly, C. Finucane, R. Burke, R. Smith, R.B. Reilly, and G. Mcdarby. 2005. Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment. EURASIP Journal on Applied Signal Processing 2005: 3156–3164.

    MATH  Google Scholar 

  • Lotte, F., M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi. 2007. A review of classification algorithms for EEG-based brain–computer interfaces. Journal of Neural Engineering 4 (2): R1.

    Article  Google Scholar 

  • Mahajan, R., D. Bansal, and S. Singh. 2014. A real time set up for retrieval of emotional states from human neural responses. International Journal of Medical, Health, Pharmaceutical and Biomedical Engineering 8 (3): 142–147.

    Google Scholar 

  • Mason, S.G., and G.E. Birch. 2000. A brain-controlled switch for asynchronous control applications. IEEE Transactions on Biomedical Engineering 47 (10): 1297–1307.

    Article  Google Scholar 

  • Millan, J.R., F. Renkens, J. Mourino, and W. Gerstner. 2004. Noninvasive brain-actuated control of a mobile robot by human EEG. IEEE Transactions on Biomedical Engineering 51 (6): 1026–1033.

    Article  Google Scholar 

  • Moon, I., M. Lee, J. Chu, and Mun, M. 2005, April. Wearable EMG-based HCI for electric-powered wheelchair users with motor disabilities. In Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005. ICRA 2005, 2649–2654. New York: IEEE.

    Google Scholar 

  • Nakayama, K., and K. Inagaki. 2006, December. A brain computer interface based on neural network with efficient pre-processing. In International Symposium on Intelligent Signal Processing and Communications, 2006. ISPACS’06, 673–676. New York: IEEE.

    Google Scholar 

  • Nakayama, K., Y. Kaneda, and A. Hirano. 2007, November. A brain computer interface based on FFT and multilayer neural network-feature extraction and generalization. In International Symposium on Intelligent Signal Processing and Communication Systems, 2007. ISPACS 2007, 826–829. New York: IEEE.

    Google Scholar 

  • Ramoser, H., J. Muller-Gerking, and G. Pfurtscheller. 2000. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering 8 (4): 441–446.

    Article  Google Scholar 

  • Reaz, M.B., M.S. Hussain, and F. Mohd-Yasin. 2006. Techniques of EMG signal analysis: detection, processing, classification, and applications. Biological Procedures Online 8 (1): 11–35.

    Article  Google Scholar 

  • Rosas-Cholula, G., J.M. Ramirez-Cortes, V. Alarcon-Aquino, P. Gomez-Gil, J.D.J. Rangel-Magdaleno, and C. Reyes-Garcia. 2013. Gyroscope-driven mouse pointer with an EMOTIV® EEG headset and data analysis based on empirical mode decomposition. Sensors 13 (8): 10561–10583.

    Article  Google Scholar 

  • Signal, N.B. 2015. NeuroSky, Inc. http://neurosky.com/Year.

  • Singla, R., and B.A. Haseena. 2013. BCI based wheelchair control using steady state visual evoked potentials and support Vector machines. International Journal of Soft Computing and Engineering (IJSCE) 3 (3): 46–52.

    Google Scholar 

  • Tanaka, K., K. Matsunaga, and H.O. Wang. 2005. Electroencephalogram-based control of an electric wheelchair. IEEE Transactions on Robotics 21 (4): 762–766.

    Article  Google Scholar 

  • Ungureanu, M., C. Bigan, R. Strungaru, and V. Lazarescu. 2004. Independent component analysis applied in biomedical signal processing. Measurement Science Review 4 (2): 18.

    Google Scholar 

  • Wolpaw, J.R., N. Birbaumer, D.J. McFarland, G. Pfurtscheller, and T.M. Vaughan. 2002. Brain–computer interfaces for communication and control. Clinical Neurophysiology 113 (6): 767–791.

    Article  Google Scholar 

  • Wolpaw, J.R., G.E. Loeb, B.Z. Allison, E. Donchin, O.F. do Nascimento, W.J. Heetderks, F. Nijboer, W.G. Shain, and J.N. Turner. 2006. BCI meeting 2005-workshop on signals and recording methods. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14 (2): 138–141.

    Google Scholar 

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Authors and Affiliations

  1. Machine Vision Laboratory, CSIR-Central Electronics Engineering Research Institute (CSIR-CEERI), Pilani, Rajasthan, India

    Swagata Das, Devashree Tripathy & Jagdish Lal Raheja

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  1. Swagata Das
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  2. Devashree Tripathy
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  3. Jagdish Lal Raheja
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Correspondence to Swagata Das .

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Das, S., Tripathy, D., Raheja, J.L. (2019). A Review on Algorithms for EEG-Based BCIs. In: Real-Time BCI System Design to Control Arduino Based Speed Controllable Robot Using EEG. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-13-3098-8_3

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