Skip to main content Accessibility help
  • Get access
    Check if you have access via personal or institutional login
  • Cited by 19
  • Cited by
    This chapter has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Wolpaw, Jonathan R. 2007. Brain-computer interfaces as new brain output pathways. The Journal of Physiology, Vol. 579, Issue. 3, p. 613.

    Mak, J.N. and Wolpaw, J.R. 2009. Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects. IEEE Reviews in Biomedical Engineering, Vol. 2, Issue. , p. 187.

    Anderson, N.R. Wisneski, K. Eisenman, L. Moran, D.W. Leuthardt, E.C. and Krusienski, D.J. 2009. An Offline Evaluation of the Autoregressive Spectrum for Electrocorticography. IEEE Transactions on Biomedical Engineering, Vol. 56, Issue. 3, p. 913.

    Schalk, Gerwin 2009. Human-Computer Interaction. Novel Interaction Methods and Techniques. Vol. 5611, Issue. , p. 616.

    McFarland, Dennis J Sarnacki, William A and Wolpaw, Jonathan R 2010. Electroencephalographic (EEG) control of three-dimensional movement. Journal of Neural Engineering, Vol. 7, Issue. 3, p. 036007.

    Randolph, Adriane B. Jackson, Melody Moore and Karmakar, Saurav 2010. Individual Characteristics and Their Effect on Predicting Mu Rhythm Modulation. International Journal of Human-Computer Interaction, Vol. 27, Issue. 1, p. 24.

    Akinci, Berna and Gencer, Nevzat Guneri 2010. Online cue-based discrimination of left / right hand movement imagination. p. 1.

    Anderson, Nicholas R. and DeVries, Elise M. 2010. Brain Computer Interface (BCI) Tools Developed in a Clinical Environment. American Journal of Electroneurodiagnostic Technology, Vol. 50, Issue. 3, p. 187.

    Khazaal Shams, Wafaa and Abdul Rahman, Abdul Wahab 2011. Characterizing autistic disorder based on Principle Component Analysis. p. 653.

    Lakey, Chad E Berry, Daniel R and Sellers, Eric W 2011. Manipulating attention via mindfulness induction improves P300-based brain–computer interface performance. Journal of Neural Engineering, Vol. 8, Issue. 2, p. 025019.

    van Erp, Jan B. F. Thurlings, Marieke E. Brouwer, Anne-Marie and Werkhoven, Peter J. 2011. Universal Access in Human-Computer Interaction. Users Diversity. Vol. 6766, Issue. , p. 610.

    Setijadi, Ary Novanda, Ori and Mengko, Tati L. R. 2011. Development of an experimental portable electroencephalograph (case study: Alpha wave detector). p. 1.

    Fager, Susan Beukelman, David R. Fried-Oken, Melanie Jakobs, Tom and Baker, John 2012. Access Interface Strategies. Assistive Technology, Vol. 24, Issue. 1, p. 25.

    Mubarok, Muhammad Husni Mengko, Tati L.R. Prihatmanto, Ary Setijadi and Indrayanto, Dan Adi 2014. Implementation of small laplacian spatial filter for MU rhythm acqusition in Bci2000. p. 1.

    Li, Qi Li, Jian Liu, Shuai and Wu, Yang 2015. Improving the Performance of P300-Speller with Familiar Face Paradigm Using Support Vector Machine Ensemble. p. 606.

    Liu, Yaru Liu, Yadong Yu, Yang Jiang, Jun Zhou, Zongtan and Hu, Dewen 2016. A Novel Brain-Computer Interface Approach Designed for Dynamic Target Selection. p. 217.

    Dey, Atanu Bhattacharjee, Sourav and Samanta, Debasis 2016. Recognition of motor imagery left and right hand movement using EEG. p. 426.

    Wu, Dongrui Lance, Brent J. Lawhern, Vernon J. Gordon, Stephen Jung, Tzyy-Ping and Lin, Chin-Teng 2017. EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features. IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 25, Issue. 11, p. 2157.

    El-Kafrawy, Noran Magdy Hegazy, Doaa and Tolba, Mohamed F. 2017. Handbook of Research on Machine Learning Innovations and Trends. p. 695.

  • Print publication year: 2006
  • Online publication date: March 2012

33 - Brain–computer interfaces for communication and control

from Section B4 - Translational research: application to human neural injury


As a communication and control system, a brain-computer interface (BCI) establishes a real-time interaction between the user and the outside world. Human BCI experience to date has been confined almost entirely to electroencephalographic (EEG) studies and short-term electrocorticographic activity (EcoG) studies. A BCI records brain signals and processes them to produce device commands. This signal processing has two stages. The first stage is feature extraction, the calculation of the values of specific features of the signals. The second stage is a translation algorithm that translates these features into device commands. The eventual extent and impact of BCI applications depend on the speed and precision of the control that can be achieved and on the reliability and convenience of their use. Simple BCI applications appear to have a secure future in their potential to make a difference in the lives of extremely disabled people.
Recommend this book

Email your librarian or administrator to recommend adding this book to your organisation's collection.

Textbook of Neural Repair and Rehabilitation
  • Online ISBN: 9780511545061
  • Book DOI:
Please enter your name
Please enter a valid email address
Who would you like to send this to *