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Abstract

Human–computer interfaces (HCI) are common in the present-day world. However, in situations where these are not doable, an alternate way is needed for realizing communication. This gives rise to what is also known as brain–computer interface (BCI) that provides a way of communication between the human brain and a machine in quite an effective way. BCIs are bliss for physically impaired patients. The dominant feature in a BCI is the neural activity generated in the brain by any stimulus. The concept includes encoding of brain signals through an electroencephalography (EEG) acquisition device and a computer to generate commands to gain control over another device which can be the computer cursor, a humanoid robot or an assistive mechanism. The process followed in the functionality of a BCI device can be explained as follows. It is initiated with an intent generated by the user which is meant to produce a speech, action, or motor activity. This intent, at the same time, gives rise to a complex signal with deterministic peaks in the brain, commonly known as EEG signals. The signals, when transmitted to the nervous system and to the muscles, result in performance of the intended action. The same intent generated signals when extracted through a BCI can be used to control a device helpful for the user/patient. In other words, a BCI bypasses the process of transmission of neural signals from the brain to the motor parts through a computer into an applicable device.

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References

  • Bayliss, J.D., and D.H. Ballard. 2000. Recognizing evoked potentials in a virtual environment. In Advances in Neural Information Processing Systems 3–9.

    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 

  • Brain Vision UK. The Brief History of Brain Computer Interfaces. http://brainvision.co.uk/news-2/the-brief-history-of-brain-computer-interfaces-2.

  • Cincotti, F., D. Mattia, F. Aloise, S. Bufalari, G. Schalk, G. Oriolo, A. Cherubini, M.G. Marciani, and F. Babiloni. 2008. Non-invasive brain–computer interface system: Towards its application as assistive technology. Brain Research Bulletin 75 (6): 796–803.

    Article  Google Scholar 

  • Donchin, E., K.M. Spencer, and R. Wijesinghe. 2000. The mental prosthesis: Assessing the speed of a P300-based brain-computer interface. IEEE Transactions on Rehabilitation Engineering 8 (2): 174–179.

    Article  Google Scholar 

  • Donoghue, J.P. 2002. Connecting cortex to machines: Recent advances in brain interfaces. Nature Neuroscience 5: 1085–1088.

    Article  Google Scholar 

  • Farwell, L.A., and E. Donchin. 1988. Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology 70 (6): 510–523.

    Article  Google Scholar 

  • Glassman, E.L. 2005. A wavelet-like filter based on neuron action potentials for analysis of human scalp electroencephalographs. IEEE Transactions on Biomedical Engineering 52 (11): 1851–1862.

    Article  Google Scholar 

  • Grabianowski ed. How Brain Computer Interfaces Work. http://computer.howstuffworks.com/brain-computer-interface2.htm.

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

    Google Scholar 

  • Haas, L.F. 2003. Hans Berger (1873–1941), Richard Caton (1842–1926), and electroencephalography. Journal of Neurology, Neurosurgery and Psychiatry 74 (1): 9.

    Article  Google Scholar 

  • Keirn, Z.A., and J.I. Aunon. 1990. A new mode of communication between man and his surroundings. IEEE Transactions on Biomedical Engineering 37 (12): 1209–1214.

    Article  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 

  • Levine, S.P., J.E. Huggins, S.L. BeMent, R.K. Kushwaha, L.A. Schuh, E.A. Passaro, M.M. Rohde, and D.A. Ross. 1999. Identification of electrocorticogram patterns as the basis for a direct brain interface. Journal of Clinical Neurophysiology 16 (5): 439.

    Article  Google Scholar 

  • McFarland, D.J., and J.R. Wolpaw. 2008. Brain-computer interface operation of robotic and prosthetic devices. Computer 41 (10)

    Google Scholar 

  • McFarland, D.J., W.A. Sarnacki, and J.R. Wolpaw. 2010. Electroencephalographic (EEG) control of three-dimensional movement. Journal of Neural Engineering 7 (3): 036007.

    Article  Google Scholar 

  • Misiti, M., Y. Misiti, G. Oppenheim, and J.M. Poggi. 2003. Les ondelettes et leurs applications. Hermès Science Publications.

    Google Scholar 

  • Perelmouter, J., and N. Birbaumer. 2000. A binary spelling interface with random errors. IEEE Transactions on Rehabilitation Engineering 8 (2): 227–232.

    Article  Google Scholar 

  • Pfurtscheller, G., C. Guger, G. MĂĽller, G. Krausz, and C. Neuper. 2000. Brain oscillations control hand orthosis in a tetraplegic. Neuroscience Letters 292 (3): 211–214.

    Article  Google Scholar 

  • Pfurtscheller, G., G.R. Muller-Putz, A. Schlogl, B. Graimann, R. Scherer, R. Leeb, C. Brunner, C. Keinrath, F. Lee, G. Townsend, and C. Vidaurre. 2006. 15 years of BCI research at Graz University of Technology: Current projects. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14 (2): 205–210.

    Article  Google Scholar 

  • Rebsamen, B., E. Burdet, C. Guan, H. Zhang, C.L. Teo, Q. Zeng, C. Laugier, and M.H. Ang Jr. 2007. Controlling a wheelchair indoors using thought. IEEE Intelligent Systems, 22 (2).

    Google Scholar 

  • Spinal Cord Injury Research Evidence (SCIRE): American Spinal Injury Association Impairment Scale (AIS). International Standards for Neurological Classification of Spinal Cord Injury. http://www.scireproject.com/outcome-measures-new/american-spinal-injury-association-impairment-scale-ais-international-standards.

  • 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 

  • Vaughan, T.M., D.J. McFarland, G. Schalk, W.A. Sarnacki, D.J. Krusienski, E.W. Sellers, and J.R. Wolpaw. 2006. The Wadsworth BCI research and development program: at home with BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14 (2): 229–233.

    Article  Google Scholar 

  • Wolpaw, J.R., and D.J. McFarland. 2004. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proceedings of the National Academy of Sciences of the United States of America 101 (51): 17849–17854.

    Article  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., D.J. McFarland, and T.M. Vaughan. 2000. Brain-computer interface research at the Wadsworth Center. IEEE Transactions on Rehabilitation Engineering 8 (2): 222–226.

    Article  Google Scholar 

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Correspondence to Swagata Das .

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Das, S., Tripathy, D., Raheja, J.L. (2019). Introduction. 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_1

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