Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
A Novel Intelligent Multiple Watermarking Schemes for the Protection of the Information Content of a Document Image Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Distribution Based EEG Baseline Classification

Authors : Gopika Gopan K., Neelam Sinha, Dinesh Babu J.

Published in: Computer Vision, Graphics, and Image Processing

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Electrical signals generated in the brain, known as Electroencephalographic (EEG) signals, form a non-invasive measure of brain functioning. Baseline states of EEG are Eyes Open (EO) and Eyes Closed (EC) relaxed states. The choice of baseline used in an experiment is of critical importance since they form a reference with which other states are measured. In Brain Machine Interface, it is imperative that the system should be able to distinguish between these states and hence the need for automated classification of EEG baselines. In the proposed method, Statistical Moments are utilized. The Moment Generating Functions (MGFs) obtained using these moments are given as features to SVM and k-NN classifiers resulting in mean accuracies of 86.71% and 86.54%. The fact that MGF is able to differentiate between these states indicate that the two states have different source distribution parameters. A Smirnov test verified that the data of two classes indeed come from different distributions.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Neovascularization Detection on Retinal Images
next chapter Texture Based Person Identification Using Dental Radiographs and Photographs in Forensic Odontology
Literature
1.
Acharya, U.R., Sree, S.V., Chattopadhyay, S., Suri, J.S.: Automated diagnosis of normal and alcoholic EEG signals. Int. J. Neural Syst. 22(03), 1250011 (2012)CrossRef
2.
Adeli, H., Ghosh-Dastidar, S., Dadmehr, N.: A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Trans. Biomed. Eng. 54(2), 205–211 (2007)CrossRef
3.
Barry, R.J., Clarke, A.R., Johnstone, S.J., Magee, C.A., Rushby, J.A.: EEG differences between eyes-closed and eyes-open resting conditions. Clin. Neurophysiol. 118(12), 2765–2773 (2007)CrossRef
4.
Bell, M.A., Fox, N.A.: Individual differences in object permanence performance at 8 months: locomotor experience and brain electrical activity. Dev. Psychobiol. 31(4), 287–297 (1997)CrossRef
5.
Berger, V.W., Zhou, Y.: Kolmogorov-Smirnov test: Overview. Wiley StatsRef: Statistics Reference Online (2005)
6.
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)MATH
7.
Ebrahimi, F., Mikaeili, M., Estrada, E., Nazeran, H.: Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1151–1154. IEEE (2008)
8.
Estrada, E., Nazeran, H., Nava, P., Behbehani, K., Burk, J., Lucas, E.: EEG feature extraction for classification of sleep stages. In: 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2004, IEMBS 2004, vol. 1, pp. 196–199. IEEE (2004)
9.
Fleck, J.I., Green, D.L., Stevenson, J.L., Payne, L., Bowden, E.M., Jung-Beeman, M., Kounios, J.: The transliminal brain at rest: baseline EEG, unusual experiences, and access to unconscious mental activity. Cortex 44(10), 1353–1363 (2008)CrossRef
10.
Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)CrossRef
11.
Janzen, T., Graap, K., Stephanson, S., Marshall, W., Fitzsimmons, G.: Differences in baseline EEG measures for add and normally achieving preadolescent males. Biofeedback Self Regul. 20(1), 65–82 (1995)CrossRef
12.
Larose, D.T.: K-nearest neighbor algorithm. Discovering Knowledge in Data: An Introduction to Data Mining, pp. 90–106 (2005)
13.
Li, L., Xiao, L., Chen, L.: Differences of EEG between eyes-open and eyes-closed states based on autoregressive method. J. Electron. Sci. Technol. China 7(2), 175–179 (2009)
14.
Miller, S., Childers, D.: Probability and Random Processes: with Applications to Signal Processing and Communications. Academic Press, Cambridge (2012)MATH
15.
Murugappan, M., Ramachandran, N., Sazali, Y., et al.: Classification of human emotion from EEG using discrete wavelet transform. J. Biomed. Sci. Eng. 3(04), 390 (2010)CrossRef
16.
Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI 2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51(6), 1034–1043 (2004)CrossRef
17.
Subasi, A.: EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst. Appl. 32(4), 1084–1093 (2007)CrossRef
18.
Tan, B., Kong, X., Yang, P., Jin, Z., Li, L.: The difference of brain functional connectivity between eyes-closed and eyes-open using graph theoretical analysis. Computational and mathematical methods in medicine 2013 (2013)
19.
Tzallas, A.T., Tsipouras, M.G., Fotiadis, D.I.: Epileptic seizure detection in EEGs using time-frequency analysis. IEEE Trans. Inf Technol. Biomed. 13(5), 703–710 (2009)CrossRef
20.
Valenzi, S., Islam, T., Jurica, P., Cichocki, A.: Individual classification of emotions using EEG. J. Biomed. Sci. Eng. 7(8), 604 (2014)CrossRef
21.
Vijayan, A.E., Sen, D., Sudheer, A.: EEG-based emotion recognition using statistical measures and auto-regressive modeling. In: 2015 IEEE International Conference on Computational Intelligence and Communication Technology (CICT), pp. 587–591. IEEE (2015)
22.
Zhu, G., Li, Y., Wen, P.P., Wang, S.: Analysis of alcoholic EEG signals based on horizontal visibility graph entropy. Brain Inf. 1(1–4), 19–25 (2014)CrossRef
Metadata
Title
Distribution Based EEG Baseline Classification
Authors
Gopika Gopan K.
Neelam Sinha
Dinesh Babu J.
Copyright Year
2017
Book
Computer Vision, Graphics, and Image Processing

Print ISBN: 978-3-319-68123-8

Electronic ISBN: 978-3-319-68124-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-68124-5_27

Premium Partner

    Image Credits