Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Brain Wave Characteristic Analysis by Multi-stimuli with EEG Channel Grouping based on Binary Harmony Search

Binary Harmony Search 기반의 EEG 채널 그룹화를 이용한 다중 자극에 반응하는 뇌파 신호의 특성 연구

  • Lee, Tae-Ju (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Park, Seung-Min (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Sim, Kwee-Bo (School of Electrical and Electronics Engineering, Chung-Ang University)
  • 이태주 (중앙대학교 전자전기공학과) ;
  • 박승민 (중앙대학교 전자전기공학과) ;
  • 심귀보 (중앙대학교 전자전기공학과)
  • Received : 2013.05.04
  • Accepted : 2013.06.20
  • Published : 2013.08.01
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Abstract

This paper proposed a novel method for an analysis feature of an Electroencephalogram (EEG) at all channels simultaneously. In a BCI (Brain-Computer Interface) system, EEGs are used to control a machine or computer. The EEG signals were weak to noise and had low spatial resolution because they were acquired by a non-invasive method involving, attaching electrodes along with scalp. This made it difficult to analyze the whole channel of EEG signals. And the previous method could not analyze multiple stimuli, the result being that the BCI system could not react to multiple orders. The method proposed in this paper made it possible analyze multiple-stimuli by grouping the channels. We searched the groups making the largest correlation coefficient summation of every member of the group with a BHS (Binary Harmony Search) algorithm. Then we assumed the EEG signal could be written in linear summation of groups using concentration parameters. In order to verify this assumption, we performed a simulation of three subjects, 60 times per person. From the simulation, we could obtain the groups of EEG signals. We also established the types of stimulus from the concentration coefficient. Consequently, we concluded that the signal could be divided into several groups. Furthermore, we could analyze the EEG in a new way with concentration coefficients from the EEG channel grouping.

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References

  1. S. H. Park, B. Hong, J. Kim, E. P. Hong, and M. Mun, "Development of the myoelectric hand with a 2 DOF auto wrist module," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 17, no. 3, pp. 824-832, Aug. 2011. https://doi.org/10.5302/J.ICROS.2011.17.8.824
  2. J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, "Brain-computer interfaces for communication and control," Clinical Neurophysiology, vol. 113, no. 6, pp. 767-791, Mar. 2002. https://doi.org/10.1016/S1388-2457(02)00057-3
  3. A. Ferreira, R. L. Silva, W. C. Celeste, T. F. Bastos Filho, and M. Sarcinelli Filho, "Human-machine interface based on muscular and brain signals applied to a robotic wheelchair," Journal of Physics: Conference Series, vol. 90, no. 1, pp. 1-8, Nov. 2007.
  4. J. Long, Y. Li, H. Wang, T. Yu, and J. Pan, "Control of a simulated wheelchar based on a hybrid brain computer interface," 34th Annual International Conference of the IEEE EMBS, Aug. 2012.
  5. S. T. Ahi, H. Kambara, and Y. Koike, "A dictionarydriven p300 speller with a modified interface," IEEE Transaction on Neural Systems and Rehabilitation Engineering, vol. 19, no. 1, pp. 6-14, Feb. 2011. https://doi.org/10.1109/TNSRE.2010.2049373
  6. Y. Chae, J. Jeong, and S. Jo, "Toward brain-actuated humanoid robots: asynchronous direct control using an EEG-based BCI," IEEE Transaction on Robotics, vol. 28, no. 5, pp. 1131-1144, Oct. 2012. https://doi.org/10.1109/TRO.2012.2201310
  7. R. Scherer, J. Faller, D. Balderas, E. V. C. Friedrich, M. Proll, and G. Muller-Putz, "Brain-computer interfacing: more than the sum of its parts," Soft Computing, vol. 17, pp. 317-331, no. 2, Feb. 2013. https://doi.org/10.1007/s00500-012-0895-4
  8. K. E. Ko and K. B. Sim, "HSA-based HMM optimization method for analyzing EEG pattern of motor imagery," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 17, no. 8, pp. 747-752, Jun. 2011. https://doi.org/10.5302/J.ICROS.2011.17.8.747
  9. T. R. Knoshe, "Determining the number of independent sources of the EEG: a simulation study on information criteria," Brain Topography, vol. 11, no. 2, pp. 111-124, 1998. https://doi.org/10.1023/A:1022202521439
  10. J. K. Kim, S. M. Park, K. E. Ko, and K. B. Sim, "Optimal EEG channel selection by gentic algorithm and binary PSO based on a support vector machine," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 19, no. 6, pp. 527-533, Jun. 2013. https://doi.org/10.5302/J.ICROS.2013.13.1864
  11. M. K. Mukul and F. Matsuno, "EEG de-noising based on wavelet-transforms and extraction of sub-band components related to movement imagination," ICCAS-SICE 2009, pp. 1605-1610, Aug. 2009.
  12. Y. Tomita, S. Ito, and Y Mitsukura, "The EEG analysis method for obtaining the felling," International Conference on Control, Automation and Systems 2008, pp. 2555-2558, Oct. 2008.
  13. S. Mun, M. C. Park, S. Park, and M. Whang, "SSVEP and ERP measurement of cognitive fatigue caused by stereoscopic 3D," Neuroscience Letters, vol. 525, no. 2, pp. 89-94, Sep. 2012. https://doi.org/10.1016/j.neulet.2012.07.049
  14. Z. W. Geem, J. H. Kim, and G. V. Loganathan, "A new heuristic optimization algorithm: Harmony search," Simulation, vol. 76, no. 2, pp. 60-68, 2001. https://doi.org/10.1177/003754970107600201
  15. Z. Kong, L. Wang, and Z. Wu, "Global adaptive harmony search algorithm and its application in rough set reduction," 2012 International Conference on Computer Science and Electronics Engineering, vol. 3, pp. 572-576, Mar. 2012.
  16. P. Yadav, R. Kumar, S. K. Panda, and C. S. Chang, "An intelligent tuned harmony search algorithm for optimisation," Information Sciences, vol. 196, pp. 47-72, Aug. 2012. https://doi.org/10.1016/j.ins.2011.12.035
  17. L. Wang, R. Yan, Y. Xu, Q. Niu, P. M. Pardalos, and M. Fei, "An improved adaptive binary Harmony search algorithm," Information Sciences, vol. 232, pp. 58-87, 2013. https://doi.org/10.1016/j.ins.2012.12.043
  18. M. Ungureanu, C. Bigan, R. Strungaru, and V. Lazarescu, "Independent component analysis applied in biomedical signal processing," Measurement Science Review, vol. 4, no. 2, 2004.
  19. R. N. Khushaba, C. Wise, S. Kodagoda, J. Luviere, B. E. Kahn, and C. Townsend, "Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking," Expert Systems with Applications, vol. 40, no. 9, pp. 3803-3812, Jul. 2013. https://doi.org/10.1016/j.eswa.2012.12.095
  20. B. Blankertz, G. Dornheg, M. Krauledat, K. R. Muller, and G. Curio, "The non-invasive berlin brain-computer interface: Fast acquisition of effective performance in untrained subjects," NeuroImage, vol. 37, no. 2, pp. 539-550, 2007. https://doi.org/10.1016/j.neuroimage.2007.01.051

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