Abstract
This study proposes a new type of features extracted from Electroencephalography (EEG) signals to distinguish between different tasks. EEG signals are collected from six children aged between two to six years old during opened and closed eyes tasks. For each time-sample, Time Difference of Arrival (TDOA) is applied to EEG time series to compute the source-temporal-features that are assigned to x, y and z coordinates. The features are classified using neural network. The results show an accuracy of around 100% for eyes open task and around (83%-95%) for eyes closed tasks for the same subject. This study highlights the use of new types of features (source-temporal features), to characterize the brain functional behavior.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Sanei, S.: Chambers: EEG signal Processing. John Wiley & Sons Ltd., England (2007)
Subha, D.P., Joseph, P.K., Acharya, U.R., Lim, C.M.: EEG signal analysis: A survey. J. Med. Syst. 34, 195–212 (2010)
Thomas, H., Budzynski, H.K., James, R.E.: Introduction to Quantitative EEG and Neurofeedback, 2nd edn. Advance Theory and Application. Academic Press, Elsevier (2008)
Schmidt, R.O.: A New Approach to Geometry of Range Difference Location. IEEE Trans.on Aerospace and Electronics System 8, 821–835 (1972)
Chan, Y.T., Ho, C.K.: A Simple and Efficient Estimator for Hyperbolic Location. IEEE Transaction on Signal Processing 42, 1905–1915 (2004)
Atkison, K.A.: An Introduction to Numerical Analysis, 2nd edn. John Wiley & Sons, New York (1989)
Baden Fuller, A.J.: Microwaves, Oxford. Pergamon Press, New York (1979)
Gabriel, S., Lau, R.W., Gabriel, C.: The dielectric properties of biological tissues:III. Parametric Models for the Dielectric Spectrum of Tissues. Phys. Med. Biol. 41, 2271–2293 (1996)
Geddes, L.A., Baker, L.E.: The specific resistance of biological material—A compendium of data for the biomedical engineer and physiologist. Medical and Biological Engineering and Computing 5, 271–293 (1976)
Grave de Peralta Menendez, R., Gonzalez Andino, S.L., Morand, S., Michel, C.M., Landis, T.: Imaging the electrical activity of the brain: ELECTRA. Hum. Brain Mapping 9, 1–12 (2000)
Rafiroiu, D., Vlad, S., Cret, L., Ciupa, R.V.: 3D Modeling of the Induced Electric Field of Transcranial Magnetic Stimulation. In: Vlad, S., Ciupa, R., Nicu, A.I. (eds.) International Conference of Advancements of Medicine and Health Care through Technology. IFMBE Proceedings, vol. 26, pp. 333–338. Springer, Heidelberg (2009)
Sumi, C., Hayakawa, K.: Mathematical expressions of Reconstructions of Conductivity and Permittivity from Current Density. International Journal of Bioelectromagnetism 9, 103–104 (2007)
Kayser, J., Crig, E., Tenke: Principle Components Analysis of Laplacian Waveforms as Generic Method for Identifiying ERP Generator Patterns: Evaluation with Auditory Oddball Tasks. Clinical Neurophysiology 117, 348–368 (2006)
Knapp, C., Carter, G.: The Generalized Correlation Method for Estimation of Time Delay. IEEE Transactions on Acoustics, Speech, and Signal Processing 4, 320–327 (1976)
Lerner, B., Guterman, H., Dinstein, I., Romen, Y.: Learning Curves and Optimization of a Multilayer Perceptron Neural Network for Chromosome Classification. World Congress on Neural Network 3, 248–253 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shams, W.K., Wahab, A., Qidwai, U.A. (2012). Detecting Different Tasks Using EEG-Source-Temporal Features. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34478-7_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-34478-7_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34477-0
Online ISBN: 978-3-642-34478-7
eBook Packages: Computer ScienceComputer Science (R0)