CFP last date
20 January 2025
Reseach Article

Power Spectrum Analysis of EEG Signals for Estimating Visual Attention

by Mitul Kumar Ahirwal, Narendra D Londhe
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 15
Year of Publication: 2012
Authors: Mitul Kumar Ahirwal, Narendra D Londhe
10.5120/5770-7993

Mitul Kumar Ahirwal, Narendra D Londhe . Power Spectrum Analysis of EEG Signals for Estimating Visual Attention. International Journal of Computer Applications. 42, 15 ( March 2012), 34-40. DOI=10.5120/5770-7993

@article{ 10.5120/5770-7993,
author = { Mitul Kumar Ahirwal, Narendra D Londhe },
title = { Power Spectrum Analysis of EEG Signals for Estimating Visual Attention },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 15 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 34-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number15/5770-7993/ },
doi = { 10.5120/5770-7993 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:31:22.964054+05:30
%A Mitul Kumar Ahirwal
%A Narendra D Londhe
%T Power Spectrum Analysis of EEG Signals for Estimating Visual Attention
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 15
%P 34-40
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The task oriented brain activity analysis and classification is a prime issue in EEG signal processing these days. The similar attempt has been done here to estimate the brain activity on the basis of power spectrum analysis. For this, the modified approach involving both Independent Component Analysis (ICA) and Principal Component Analysis (PCA) methodologies has been used in this paper to investigate the behavior of brain's electrical activity for a simple case of visual attention. The proposed method of EEG classification can be very useful in predicting the action or the intention of action performed on the basis of EEG which leads to more development in brain computer interface. The EEG data has been referred from a website and the mathematical tool for EEG analysis called EEGLAB has been used to perform work in this paper.

References
  1. Sanei, S. , and Chambers, J. A. , EEG signal processing, John Wiley & Sons Ltd. 2007.
  2. Pfurtschellera, G. , Lopes, F. H. , Silva, Da. , "Event-related EEG/MEG synchronization and desynchronization: basic principles", Clinical Neurophysiology,1999.
  3. Schalk, G. , McFarland, D. J. , Hinterberger, T. , Birbaumer, N. , Wolpaw, J. R. , "BCI2000: a general-purpose brain-computer interface (BCI) system," IEEE Transactions on Biomedical Engineering, 2004
  4. Fabiani, G. , McFarland, D. J. , Wolpaw, J. R. , and Pfurtscheller, G. , "Conversion of eeg activity into cursor movement by brain-computer interface (bci)", IEEE Trans. on Neural Systems and Rehabilitation Eng. , 2004 .
  5. Kiymik, K. M. , Akin, M. , and Subasi, A. , "Automatic recognition of alertness level by using wavelet transform and artificial neural network", Journal of Neuroscience Methods, 2004.
  6. Jung, T. P. , Makeig, S. , Stensmo, M. , Sejnowski, T. J. , "Estimating Alertness from EEG power spectrum", IEEE Transaction on Biomedical Engineering, 1997.
  7. Abdul-latif, A. A. , Cosic, I. , Kumar, D. K. , Polus, B. , and Costa, C. Da. 2004. , Power changes of EEG signals associated with muscle fatigue: the root mean square analysis of EEG bands, Intelligent Sensors, Sensor Networks and Information Conference.
  8. Luzheng, Bi. , Zhang, R. , Zhilong, C. 2007 Study on Real-time Detection of Alertness Based on EEG, IEEE/ICME International Conference on Complex Medical Engineering.
  9. Zhang, A. , Yang, B. and Huang, L. 2008 Feature Extraction of EEG Signals Using Power Spectral Entropy, Paper Presented at the BioMedical Engineering and Informatics Conference BMEI.
  10. Hamid N. H. A. , Sulaiman, N. , Aris, S. M. A. , Murat Z. H. , and Taib, M. N. , 2010 Evaluation of Human Stress Using EEG Power Spectrum, Paper Presented at the International Colloquium on Signal Processing & Its Applications (CSPA).
  11. Lias, S. , Sulaiman, N. , Murat, Z. M. , and Taib, M. N. 2010 IQ Index using Alpha-Beta Correlation of EEG Power Spectrum Density (PSD), Paper Presented at the IEEE Symposium on Industrial Electronics and Applications (ISIEA) Penang, Malaysia.
  12. EEG/ERP data vailable for free public download. Obtained through the Internet: http: //sccn. ucsd. edu /~arno/fam2data / publicly _available_EEG_data. html, [accessed 15/01/2011].
  13. EEGLAB a Matlab tool box for EEG analysis Obtained through the Internet:http://sccn. ucsd. edu [accessed 12/11/2010].
  14. Makeig, S. , Bell, A. J. , Sejnowski, T. J. , and Jung, T. P. , "Independent Component Analysis of Electroencephalographic Data", Advances in Neural Information Processing Systems. MlT Press, Cambridge MA, PP. 145-151, 1996.
  15. Van Dun, B. ; Wouters, J. ; Moonen, M. , "Improving Auditory Steady-State Response Detection Using Independent Component Analysis on Multichannel EEG Data," IEEE Transactions on Biomedical Engineering, vol. 54, no. 7, pp. 1220-1230, July 2007
  16. Jung, T. P. , Humphries, C. , Lee, T. W. , Makeig, S. , McKeown, M. J. , Iragui, V. , and Sejnowski, T. J. , "Extended ICA removes artifacts from electroencephalographic recordings", Advances in Neural Information Processing Systems , MIT Press, Cambridge, 1998.
  17. Jolliffe, I. T. , Principal Component Analysis, 2nd Edition. Spinger, 2002.
  18. Lei, C. , Jie L. , Yaoru S. , Zhu, H. , Yan, C. 2010 EEG-based vigilance analysis by using fisher score and PCA algorithm, IEEE International Conference on Progress in Informatics and Computing (PIC).
  19. Oh, C. , Kim, M. S. , and Lee, J. J. 2006, EEG signal classification based on PCA and NN, Paper Presented at the SICE-ICASE International Joint Conference, Korea.
Index Terms

Computer Science
Information Sciences

Keywords

Eeg Ica Pca Power Spectrum