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Reseach Article

Classification of Mental Tasks using EEG and Hierarchical Classifier employing Optimised Neural Networks

by Madhuri N. Bawane, K. M. Bhurchandi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 133 - Number 13
Year of Publication: 2016
Authors: Madhuri N. Bawane, K. M. Bhurchandi

Madhuri N. Bawane, K. M. Bhurchandi . Classification of Mental Tasks using EEG and Hierarchical Classifier employing Optimised Neural Networks. International Journal of Computer Applications. 133, 13 ( January 2016), 33-41. DOI=10.5120/ijca2016908163

@article{ 10.5120/ijca2016908163,
author = { Madhuri N. Bawane, K. M. Bhurchandi },
title = { Classification of Mental Tasks using EEG and Hierarchical Classifier employing Optimised Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 133 },
number = { 13 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 33-41 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016908163 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:31:07.806618+05:30
%A Madhuri N. Bawane
%A K. M. Bhurchandi
%T Classification of Mental Tasks using EEG and Hierarchical Classifier employing Optimised Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 133
%N 13
%P 33-41
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

With recent advances in Electroencephalogram (EEG) signal processing and biomedical instrumentation, brain machine interfaces are used for rehabilitation of people suffering from neuromuscular disorders. This paper presents a novel method employing Hierarchical classifier using optimised Neural Networks to classify left-hand movement, right-hand movement and word generation using EEG signals. One of the most important components of brain computer interface (BCI) is feature extraction of EEG signals. Power spectral density (PSD) is used for feature extraction from EEG signals. The proposed pre-processing and reconfiguration of PSD samples make them more discriminative & yield appropriately organized feature vectors. The adaptation of network weights using Comprehensive Learning Particle Swarm Optimization (CLPSO) is proposed to improve the performance of Neural Network (NN). Further, the two level hierarchical neural network is used to enhance the discriminative property of the features and hence better classification accuracy is achieved. Results are verified on BCI benchmarking database as well as our own experimental database. Results obtained using the proposed methods are compared with other contemporary methods such as Linear Discrimination analysis (LDA), neural networks based on improved particle swarm optimization (IPSONN) and to a recently proposed approach based on Evidence-based combining classifier. It is found that the proposed method outperforms all the contemporary techniques for the multi-task EEG classification. This new method can be easily extended to other multitask BCI applications.

  1. J.R. Wolpaw, N. Birbaumer, W.J. Heetderks, D.J. McFarland, P.H. Peckham, G. Schalk, E. Donchin, L.A. Quatrano, C.J. Robinson, T.M. Vaughan,2000. Brain–computer interface technology: A review of the first international meeting, IEEE Trans. Rehab. Eng. 8, 164–173 2000.
  2. Wolpaw J.R., McFarland D.J., Vaughan T. et al, “The Wadsworth Center brain–computer interface (BCI) research and development program,” IEEE Trans Neural Syst Rehabil Eng, Vol. 11, pp. 204-207, 2003.
  3. Kositsky M., Karniel A., Alford S. et al, “Dynamical dimension of a hybrid neurorobotic system,” IEEE Trans Neural Syst Rehabil Eng, Vol. 11, pp. 155-159, 2003.
  4. Moore M., “Real-world applications for brain–computer interface technology,” IEEE Trans Neural Syst Rehabil Eng, Vol. 11, pp. 162-165, 2003.
  5. Taylor D., Tillery S., Schwartz A., “Information conveyed through brain-control: cursor vs robot,” IEEE Trans Neural Syst Rehabil Engg, Vol. 11, pp. 195-199, 2003.
  6. C. Guger, A. Schlo¨gl, C. Neuper, D. Walterspacher, T.Strein, G.Pfurtscheller, “Rapid prototyping of an EEG based brain computer interface (BCI)”, IEEE Transactions on Rehabilitation Engineering. vol. 9 no. 1, pp 49–58, March 2001.
  7. G. Pfurtscheller, C. Neuper, “Motor imagery activates primary sensorimotor area in humans” , Neuroscience Letters, vol. 239, no. 2-3, pp. 65–68, 1997
  8. Wang Z, Maier A, Leopold DA, Logothetis NK, Liang H. “Single-trial evoked potential estimation using wavelets”, Computers in Biology and Medicine, 37, 463 – 473, 2007
  9. M. Fatourechi1, S.G. Mason, G.E. Birch and R.K. Ward, “A wavelet-based approach for the extraction of event related potentials from EEG”, IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2004. vol.2 pp 737-40, 17-21 May 2004,
  10. Liu Mingyu 1, Ji Hongbing1, Zhao Chunhong, “ Event Related Potentials Extraction from EEG Using Artificial Neural Network ”, Proceedings of the 2008 Congress on Image and Signal Processing, Volume 01, pp 213-215, 2008
  11. Krusienski D.J., McFarland D.J. and Wolpaw J.R., “An evaluation of autoregressive spectral estimation model order for brain-computer interface applications,” IEEE EMBS Ann Int Conf 2006, pp. 1323-1326, 2006.
  12. McFarland D.J., Wolpaw J.R., “Sensorimotor rhythm-based brain–computer interface (BCI): model order selection for autoregressive spectral analysis,” J Neural Eng, Vol. 5, pp. 155-162, 2008.
  13. N-J Huan and R. Palaniappan, “Classification of Mental Tasks using Fixed and Adaptive Autoregressive Models of EEG Signals,” Proc. of 26th Annual Intl. Conf. IEEE EMBS, CA, USA, pp 507 – 510, 2004.
  14. Hoffmann U., Vesin J.M., Ebrahimi T., “Spatial filters for the classification of event-related potentials,” Proc 14th Eur Symp Artif Neural Networks, pp. 47-52, 2006.
  15. Liao X., Yao D.Z., Wu D. et al, “Combining Spatial Filters for the Classification of Single-Trial EEG in a Finger Movement Task,” IEEE Trans Biomed Eng, Vol. 54, pp. 821-831, 2007.
  16. Maan M. Shaker, “EEG Waves Classifier using Wavelet Transform and Fourier Transform” , International Journal of Biological, Biomedical and Medical Sciences Vol 1, No 2, 2006.
  17. Martina Tolić, Franjo Jović, “Classification of wavelet transformed EEG signals with neural network for Imagined mental and motor tasks”, Kinesiology; Vol.45 Issue 1, pp. 130-138, 2013.
  18. Vladimir Bostanov, “ BCI competition 2003-data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram” , IEEE Transactions on Biomedical Engineering, vol. 51, no.6, pp. 1057- 1061, June 2004.
  19. Kouhyar Tavakolian, A. M. Nasrabadi, Siamak Rezaei, “Selecting Better EEG Channels For Classification Of Mental Tasks’’, IEEE International Symposium on Circuits and System- ISCAS2004, 23-26 May 2004,
  20. Pfurtscheller G., Neuper C., Guger C. et al, “Current trends in Graz Brain-Computer Interface (BCI) research,” IEEE Trans Rehabil Eng, Vol. 8, pp. 216-219, 2000.
  21. M.N.Bawane, K.M.Bhurchandi , “Classification of Mental Task Based on EEG Processing Using Self Organising Feature Map ”, Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC),vol.2, pp 240-244, Aug. 2014
  22. Xiaoou Li; Xun Chen; Yuning Yan; Wenshi Wei; Wang , Z. Jane , “ Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine ” Sensors 2014, Vol. 14 Issue 7 , pp.12784-12802, July2014.
  23. C. Lin and M. Hsieh, “Classification of mental task from EEG data using neural networks based on particle swarm optimization”, Neurocomputing, vol. 72, pp 1121-1130 , Jan. 2009
  24. Emmanuel Morales-Flores, Juan Manuel Ramírez-Cortés, Pilar Gómez-Gil, Vicente Alarcón-quino, “Mental Tasks Temporal Classification Using an Architecture Based on ANFIS and Recurrent Neural Networks” , Recent Advances on Hybrid Intelligent Systems , volume 451, pp 135-146, 2013
  25. T. Wang, J. Deng, and B. He, “Classifying EEG-based motor imagery tasks by means of time-frequency synthesized spatial patterns”, Clinical neurophysiology official journal of the International Federation of Clinical Neurophysiology, vol. 115, pp 2744-2753 , Dec. 2004
  26. Hsu WY, “ EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features ”, Journal of Neuroscience Methods, 295-302 June 2010
  27. S. Chiappa and D. Barber, “ EEG classification using generative independent component analysis” Neurocomputing , 69(7-9), pp. 769-777, 2006
  28. Proakis , J.G. and Manolakis, D.G. , Digital Signal Processing: Principles, Algorithms, And Applications, 4/E , Pearson Education India , 2007
  29. Kennedy, J. and Eberhart, R.C.: ‘Particle swarm optimization’, in Proc. IEEE Int. Conf. Neural Networks, 1995, pp. 1942-1948.
  30. Bronzino J.D. “Principles of Electroencephalography ”. In The Biomedical Engineering Handbook;CRC Press LLC: Boca Raton, FL, USA, 2000
  31. J. J. Liang, A. K. Qin, P. N. Suganthan and S. Baskar, “ Comprehensive learning particle swarm optimizer for global optimization of multimodal functions ”. IEEE Transactions On Evolutionary Computation, Vol. 10, No. 3, June 2006 281–295.
  32. M. Clerc, J. Kennedy, “ The particle swarm-explosion , stability and convergence in a multidimensional complex space” , IEEE Transactions on Evolutionary Computation 6 (1) (2002).
  33. BCI Competition III Data Set V Mental Imagery, Multi-Class, , 2004[Online]. Available: competition/iii/desc_V.html
  34. C. W. Therrien, “ Discrete Random Signals and Statistical Signal Processing ”, New Jersey: Prentice-Hall,2004
  35. Erman Acar, 2011. “Classification of motor imagery tasks in EEG signal and Its application to a brain-computer interface for Controlling assistive environmental devices”, MS thesis, Middle East Technical University,Turkey, February 2011
  36. R. Aler, I. Galván, and J. Valls, “Evolving spatial and frequency selection filters for brain-computer interfaces,” in 2010 IEEE World Congress Comput. Intell. WCCI 2010, Barcelona, Spain, pp. 1–7, 2010
  37. S.R. Kheradpisheh, A. Nowzari-Dalini, R. Ebrahimpour, M. Ganjtabesh, “ An evidence-based combining classifier for brain signal analysis”, PloS one 9 (1) (2014) e84341
Index Terms

Computer Science
Information Sciences


Hierarchical Classifier Successive input resampling CLPSO