A Comparison of Real-Time Extraction between Chebyshev and Butterworth Method for SSVEP Brain Signals

Authors

  • Dwi Esti Kusumandari A Technical Implementation Unit for Instrumentation Development, Indonesian Institute of Sciences, Indonesia
  • Taufik Hidayat A Technical Implementation Unit for Instrumentation Development, Indonesian Institute of Sciences, Indonesia
  • Arjon Turnip A Technical Implementation Unit for Instrumentation Development, Indonesian Institute of Sciences, Indonesia

Keywords:

Butterworth, Chebyshev, EEG-SSVEP, Feedforward Neural Networks,

Abstract

In this paper, a comparison of real-time extraction using the IIR Chebyshev of 4 order and the IIR Butterworth of 6 order methods is proposed. In the Experiment, the steady-state visual evoked potential with stimuli frequencies of 7,5 10, 15, and 20 Hz is used to control the wheelchair directions (i.e., stop, forward, right, and left). The data were collected from a session in which fourteen subjects with age about 24±2 years were tested. The total average classification accuracy of 82% and 62.2% for Chebychev and Butterworth extraction method are achieved. The higher average classification accuracy of 100% and 92.8% for both methods, respectively, are obtained for forward direction (8.75-12.5Hz).

References

P. A. Rutecki, "Neuronal excitability: voltage-dependent currents and synaptic transmission.", Journal of Clinical Neurophysiology. 9: 195– 211.

F. Beverina, G. Palmas, S. Silvoni, F. Piccione, and S. Giove, "User adaptive BCIs: SSVEP and P300 based interfaces," PsychNol. J. vol. 1, pp. 331–54, 2003.

S. T. Morgan, J. C. Hansen, and S. A. Hillyard, “Selective attention to stimulus location modulates the steady-state visual evoked potential,” Neurobiology, vol. 93, pp. 4770-4774, 1996.

M. M. Muller and S. A. Hillyard, “Effects of spatial selective attention on the steady state visual evoked potential in the 20-28 hz range,” Cognitive BrainResearch, vol. 6, pp. 249-26, 1997.

R. Singla, A. Khosla, and R. Jha, “Influence of stimuli colour in SSVEP-based BCI wheelchair control using support vector machines,” J Med. Eng. Technol., vol. 38(3), pp. 125-34, Feb. 2014.

T. Kaufmann, A. Herweg, and A. Kübler, “Toward brain-computer interface based wheelchair control utilizing tactually-evoked eventrelated potentials,” Journal of NeuroEngineering and Rehabilitation, vol. 11:7, 2014

D. Huang, D. Y. Fei, W. Jia, X. Chen, and O. Bai, “Electroencephalography (EEG)-based brain-computer interface (BCI): a 2-D virtual wheelchair control based on event-related desynchronization/synchronization and state control,” IEEE Trans Neural Syst Rehabil Eng., vol. 20(3), pp. 379-88, 2012.

A. Turnip, K. S. Hong, and M. Y. Jeong, “Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis,” BioMedical Engineering OnLine,” vol. 10(83), 2011.

A. Turnip and K. S. Hong, “Classifying mental activities from EEGP300 signals using adaptive neural network,” Int. J. Innov. Comp. Inf. Control, vol. 8(7), 2012.

A. Turnip, S. S. Hutagalung, J. Pardede, and, D. Soetraprawata, "P300 detection using multilayer neural networks based adaptive feature extraction method", International Journal of Brain and Cognitive Sciences, vol. 2, no. 5, pp. 63-75, 2013.

A. Turnip and M. Siahaan, “Adaptive Principal Component Analysis based Recursive Least Squares for Artifact Removal of EEG Signals,” Advanced Science Letters, vol. 20, no.10-12, pp. 2034-2037(4), October 2014.

A. Turnip and D. E. Kusumandari,“Improvement of BCI performance through nonlinear independent component analysis extraction,” Journal of Computer, vol. 9, no. 3, pp. 688-695, March 2014.

A. Turnip,D. Soetraprawata, and D. E. Kusumandari, “A Comparison of Extraction Techniques for the rapid EEG-P300 Signals,” Advanced Science Letters, vol. 20, no. 1, pp. 80-85(6), January, 2014.

A. Turnip, D. Soetraprawata, D. E. Kusumandari, “A comparison of EEG processing methods to improve the performance of BCI,” International Journal of Signal Processing Systems, 1 (1), 63-67

A. Turnip and D. Soetraprawata, “The Performance of EEG-P300 Classification using Backpropagation Neural Networks,” Journal of Mechatronics, Electrical Power, and Vehicular Technology, 4 (2), 81- 88

A. Turnip and D. Soetraprawata, “Electrooculography Detection from Recorded Electroencephalogram Signals by Extended Independent Component Analysis,” Advanced Science Letters, 21 (2), 173-176, 2015.

A. Turnip, “Comparison of ICA-Based JADE and SOBI Methods EOG Artifacts Removal”, Journal of Medical and Bioengineering, Vol 4 (6), 2015.

A. Belitski, J. Farquhar, and P. Desain, “P300 audio-visual speller,” Journal of Neural Engineering, vol. 8(2), 025022, 2011.

J. P. Rosenfeld, B. Cantwell, V. T. Nasman, V. Wojdac, S. Ivanov, and L. Mazzeri, “A modified, event-related potential-based guilty knowledge test,” Int. J. Neurosci., 42(1-2), 157-161, 1988.

M. Kh. Hazrati and A. Erfanian, “An online EEG-based braincomputer interface for controlling hand grasp using an adaptive probabilistic neural network,” Medical Engineering & Physics, vol. 32(7), pp. 730-739, 2010.

Turnip, A., Kusumandari, D. E., Rizgyawan, M. I., Sihombing, P. “Design of Extraction Method of SSVEP Brain Activity with IIR Chebyshev,” The 5th International Conference on Instrumentation, Control and Automation 2017 (ICA 2017), August, 9-11, Yogyakarta, Indonesia.

Turnip, A., Rizgyawan, M. I., Kusumandari, D. E., Yanyoan, S., and Mulyana, E., “Real Time Classification of SSVEP Brain Activity with Adaptive Feedforward Neural Networks,” International Conference on Information Technology, Computer, and Electrical Engineering, Semarang, Indonesia, October 19-21, 2016.

J.M. Zurada, Introduction to Artificial Neural Systems West Publishing Company, St. Paul, MI, 1992.

P. Sajda, A. Gerson, R. Müller, B. Blankertz, and L. Parra, “A Data Analysis Competition to Evaluate Machine Learning Algorithms for Use in Brain-Computer Interfaces,” Computer Journal of IEEE Trans Neural Systems, vol. 11, no. 2, pp. 184-185, 2003.

Downloads

Published

2018-05-31

How to Cite

Kusumandari, D. E., Hidayat, T., & Turnip, A. (2018). A Comparison of Real-Time Extraction between Chebyshev and Butterworth Method for SSVEP Brain Signals. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-3), 135–139. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4206

Issue

Vol. 10 No. 2-3: Technology Innovation in Electrical, Electronics and Information Technology

Section

Articles

License

TRANSFER OF COPYRIGHT AGREEMENT

The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):

  1. All proprietary right other than copyright, such as patent rights
  2. The right to make further copies of all or part of the published article for my use in classroom teaching
  3. The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
  4. The right to make copies of the published work for internal distribution within the institution that employs me

I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.