Skip to main content
SpringerLink
Log in
Book cover

Computational Intelligence for Big Data Analysis pp 181–197Cite as

Fundamentals of Brain Signals and Its Medical Application Using Data Analysis Techniques

  • Chapter

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 19))

Abstract

In this chapter, the various data analysis techniques devoted to the development of brain signals controlled interface devices for the purpose of rehabilitation in a multi-disciplinary engineering is presented. The knowledge of electroencephalogram (EEG) is essential for the neophytes in the development of algorithms using EEG.Most literatures, demonstrates the application of EEG signals and no much definite study describes the various components that are censorious for development of interface devices using prevalent algorithms in real-time data analysis. Therefore, this chapter covers the EEG generation, various components of EEG used in development of interface devices and algorithms used for identification of information from EEG.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Hinrichs, H.: Evoked Potentials. In: Moore Jr., J.E., Maitland, D.J. (eds.) Biomedical Technology and Devices. CRC Press (2013)

    Google Scholar 

  2. Fabiani, M., Gratton, G., Coles, M.: Event-related brain potentials. In: Cacioppo, J., Tassinary, L., Bernston, G. (eds.) Handbook of Psychophysilogy. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  3. Picton, T.W.: Electrophysiology of Mind: Event-Related Brain Potentials and Cognition. In: Rugg, M.D., Coles, M.G.H. (eds.) Psychophysilogy. Oxford University Press, Oxford (1995)

    Google Scholar 

  4. Bickford, R.D.: Electroencephalography. In: Adelman, G. (ed.) Encyclopedia of Neuroscience. Birkhäuser, Cambridge (1987)

    Google Scholar 

  5. Roman-Gonzalez, A.: EEG Signal Processing for BCI Applications. In: Hippe, Z.S., Kulikowski, J.L., Mroczek, T. (eds.) Human – Computer Systems Interaction: Backgrounds and Applications 2. AISC, vol. 98, pp. 571–591. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  6. http://www.sccn.ucsd.edu/~arno/fam2data/publicly_available_EEG_data.html (cited April 5, 2013)

  7. http://www.meb.uni-bonn.de/epileptologie/science/physik/eegdata.html (cited February 16, 2013)

  8. Blankertz, B., Sannelli, C., Halder, S., Hammer, E.M., Kubler, A., Muller, K.R., Curio, G., Dickhaus, T.: Neurophysiological predictor of SMR-based BCI performance. Journal of Neuroimage (2010), doi:10.1016/j.neuroimage.2010.03.022

    Google Scholar 

  9. Cao, L.J., Chua, K.S., Chong, W.K., Lee, H.P., Gu, Q.M.: A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing (2003), doi:10.1016/S0925-2312(03)00433-8

    Google Scholar 

  10. Cheng, M., Gao, X., Gao, S., Xu, D.: Design and Implementaion of a Brain-Computer Interface with High Transfer Rates. IEEE Trans. Biomedical Engineering (2002), doi:10.1109/TBME.2002.803536

    Google Scholar 

  11. Cheyne, D.W.: MEG Studies of Sensorimotor Rhythms: A review. J. Experimental Neurology (2012), doi:10.1016/j.expneurol.2012.08.030

    Google Scholar 

  12. Cichocki, A., Washizawa, Y., Rutkowski, T., Bakardjian, H., Phan, A.: Noninvasive BCIs: Multiway Signal-Processing array decompositions. IEEE Computer Society (2008), doi:10.1109/MC.2008.431

    Google Scholar 

  13. Cincotti, F., Mattia, D., Aloise, F., Bufalari, S., Schalk, G., Oriolo, G., Cherubini, A., Marciani, M.G., Babiloni, F.: Non-invasive brain-computer interface system: towards its application as assistive technology. J. Brain Res. Bull. (2008), doi:10.1016/j.brainresbull.2008.01.007

    Google Scholar 

  14. Garaizar, P., Vadillo, M.A., Lopez-de-lpina, D., Matute, H.: Measuring software timing errors in the presentation of visual stimuli in cognitive neuroscience experiments. Plos One (2014), doi:10.1371/journal.pone.0085108

    Google Scholar 

  15. Hammond, D.C.: What is Neurofeedback? Journal of Neurotheraphy (2011), doi:10.1080/10874208.2011.623090

    Google Scholar 

  16. Krusienski, D.J., Grosse-Wentrup, M., Galan, F., Coyle, D., Miller, K.J., Forney, E., Anderson, C.W.: Critical issues in State-of-the-art brain-computer interface signal processing. J. Neural Engineering (2011), doi:10.1088/1741-2560/8/2/025002

    Google Scholar 

  17. Nijholt, A., Tan, D.: Brain-computer interfacing for intelligent systems. IEEE Computer Society (2008), doi:10.1109/MIS.2008.41

    Google Scholar 

  18. Pichiorri, F., De. VicoFallani, F., Cincotti, F., Babiloni, F., Molinari, M., Kleih, S.C., Neuper, C., Kubler, A., Mattia, D.: Sensorimotor rhythm-based brain-computer interface training: the impact on motor cortical responsiveness. J. Neural Eng (2011), doi:10.1088/1741-2560/8/2/025020

    Google Scholar 

  19. Shyu, K., Chiu, Y., Lee, P., Liang, J., Peng, S.: Adaptive SSVEP-Based BCI System With Frequencyand Pulse Duty-Cycle Stimuli Tuning Design. IEEE Trans. Neural Systems and Rehabilitation Engineering (2013), doi:10.1109/TNSRE.2013.2265308

    Google Scholar 

  20. Zhang, Y., Xu, P., Liu, T., Hu, J., Zhang, R., Yao, D.: Multiple Frequencies Sequential Coding for SSVEP-Based Brain-Computer Interface. Plos One (2014), doi:10.1371/journal.pone.0029519

    Google Scholar 

  21. Guan, C., Thulasidas, M., Wu, J.: High performance P300 speller for brain-computer interface. In: Proc. IEEE Int. Workshop on Biomedical Circuits and System (2004), doi:10.1109/BIOCAS.2004.1454155

    Google Scholar 

  22. Hu, J., Si, J., Olson, B.P., He, J.: Principle component feature detector for motor cortical control. In: Proc. 26th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (2004), doi:10.1109/IEMBS.2004.1404123

    Google Scholar 

  23. Heinrich, H., Gevensleven, H., Strehl, U.: Annotation: Neurofeedback-train your brain to train behavior. J. of Child Psychology and Psychiatry 48, 3–16 (2007)

    Article  Google Scholar 

  24. Rangaswamy, M., Porjesz, B., Chorlian, D., Wang, B.K., Jones, K.A., Bauer, L.O.: Beta power in the EEG of alcoholics. J. Biol. Psychiatry 52, 831–842 (2002)

    Article  Google Scholar 

  25. Anderson, C.W., Devulapalli, S.V., Stolz, E.A.: Signal classification with different signal representations. In: Proc. IEEE Workshop on Neural Networks for Signal Processing (1995), doi:10.1109/NNSP.1995.514922

    Google Scholar 

  26. Bayliss, J.D., Ballard, H.D.: Single trial P300 recognition in a virtual environment. In: Proc. Int. ICSC Symp. on Soft Computing in Biomedicine, Genova, Italy (1998)

    Google Scholar 

  27. Friedman, D., Johnson Jr., R.: Event-Related Potential (ERP) Studies of Memory Encoding and Retrieval: A Selective Review. Microscopy Research and Techniques 51, 6–28 (2000)

    Article  Google Scholar 

  28. Lukas, S.E., Mendelson, J.H., Benedikt, R.: Electroencephalographic correlates of marihuana-induced euphoria. Drug and Alcohol Dependence 37, 131–140 (1995)

    Article  Google Scholar 

  29. Pfurtscheller, G., Lopes da Silva, F.H.: Event-related EEG/MEG synchronization and desynchronization: Basic principles. Clinical Neurophysiology 110, 1842–1857 (1999)

    Article  Google Scholar 

  30. Teplan, M.: Fundamentals of EEG measurement. Measurement Science Review 2, 1–11 (2002)

    Google Scholar 

  31. Luck, S.J.: An Introduction to the Event-Related Potential Technique, 2nd edn. MIT Press, Cambridge (2005)

    Google Scholar 

  32. Niedermeyer, E., Lopes da Silva, F.H.: Electroencephalography: Basic principles, clinical applications and related fields, 3rd edn. Williams & Wilkins, Philadelphia (1993)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical Engineering, VIT University, Vellore, 632014, Tamilnadu, India

    P. Geethanjali

Authors
  1. P. Geethanjali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Geethanjali .

Editor information

Editors and Affiliations

  1. School of Computing Sciences and Engineering, VIT University, Vellore, India

    D.P. Acharjya

  2. Department of Information and Communication Technology, Fakir Mohan University, Balasore, India

    Satchidananda Dehuri

  3. Corporate Technology Office, Tata Consultancy Services, Mumbai, Maharashtra, India

    Sugata Sanyal

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Geethanjali, P. (2015). Fundamentals of Brain Signals and Its Medical Application Using Data Analysis Techniques. In: Acharjya, D., Dehuri, S., Sanyal, S. (eds) Computational Intelligence for Big Data Analysis. Adaptation, Learning, and Optimization, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-16598-1_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-16598-1_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16597-4

  • Online ISBN: 978-3-319-16598-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation