Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지
DOI QR코드

DOI QR Code

Generation of Control Signal based on Concentration Detection using EEG signal

뇌파 집중력 분석을 이용한 제어 신호 발생

  • Kang, ByeongKeun (Department of Electronic and Information Engineering, Seoul National University of Science and Technology) ;
  • Yoon, Gilwon (Department of Electronic and IT Media Engineering, Seoul National University of Science and Technology)
  • 강병근 (서울과학기술대학교 전자정보공학과) ;
  • 윤길원 (서울과학기술대학교 전자IT미디어공학과)
  • Received : 2013.08.09
  • Accepted : 2013.11.22
  • Published : 2013.12.25
Download PDF
⟨ Previous Next ⟩

Abstract

Control signal generated from EEG (electro-encephalogram) can be used in many applications. In our study, for the purpose of developing practical instruments, a single channel system of providing reliable on/off signals was investigated since a multi-channel system can be bulky and expensive. Brainwaves in alpha, beta and theta bands were analyzed in order to extract reliable control signals when the concentration state reached. Rest and concentration states were differentiated based on power spectrum and histogram analysis. A better performance was obtained when the ratio between the beta and theta bands was used compared to the theta band only. In general, the longer the rest period before concentration, the lower success rate was. In addition, longer rest time produced longer detection time. Though there were individual differences, in case of 10-second rest time, a success rate of 91% and a detection time of 20.2 seconds was achieved on average.

뇌전도 분석에 의한 제어신호의 검출은 다양한 분야에 활용될 수 있다. 다채널 뇌파 연구는 측정 시스템이 복잡해지고 착용이 불편해진다는 단점이 있어서 본 논문에서는 실용적인 응용을 우선으로 하였고 단 채널 기반으로 집중에 의한 on/off 제어 신호를 신뢰성 있게 검출할 수 있는 방법을 연구하였다. 평상시 휴식 상태와 집중하였을 때의 알파, 베타 및 세타파의 대역 신호를 분석하였으며 이 때 파워스펙트럼과 히스토그램에서의 차이를 검출하였다. 세타파를 이용하였을 때 보다 베타/세타를 이용해 집중력 검출을 하는 것이 더 좋은 결과를 나타내었다. 세타파만을 이용하였을 때보다 평균 검출 시간이 단축되었으며 또한 집중 전 휴식시간이 길어질수록 검출 성공률이 낮아지며, 검출 시간도 상대적으로 길어졌다. 휴식시간 10초의 경우 개인마다 검출 성능의 차이는 있었지만 평균 91%의 검출 성공률과 검출 시간은 평균 20.2초의 결과를 얻었다.

Keywords

References

  1. F. Rosler, "From single-channel recordings to brain-mapping devices," History of Psychology, Vol. 8, no. 1, pp. 95-117, 2005. https://doi.org/10.1037/1093-4510.8.1.95
  2. K. R. Godfrey and D. M. Bruce, "Computer analysis of brainwaves," Electronics & Power, Vol. 22, issue. 8, pp. 510-514, August 1976. https://doi.org/10.1049/ep.1976.0262
  3. N. Sulaiman, N. H. A. Hamid, Z. Hj Murat and M. N. Taib, "Initial investigation of human physical stress level using brainwaves," in Proc. of IEEE Student Conf. on Research and Development, pp. 230-233, UPM Serdang, Malaysia, Nov. 2009.
  4. K. S. Park, B. G. Min, C. W. Lee, "A Study on Computer-Assisted Automatic Spike Detection System in EEG Signal of Epileptic Patients," Journal of the IEEK, Vol. 17, no. 6, pp. 28-32, December 1980.
  5. L. Kirkup, A. Searle, A. Craig, P. Mclsaac, P Moses, "EEG-based system for rapid on-off switching without prior learning", Medical and Biological Engineering and Computing, vol. 35, pp. 504-509, 1997. https://doi.org/10.1007/BF02525531
  6. J. R. Wolpaw, D. J. McFarland, "Multichannel EEG-based brain-computer communication," Electroenceph. clin. Neurophysiol, vol. 90, pp. 444-449, 1994. https://doi.org/10.1016/0013-4694(94)90135-X
  7. A. Erfanian and A. Erfani, "ICA-based classification scheme for EEG-based brain-computer interface: the role of mental practice and concentration skills," in Proc. of the 26th Annual International Conf. on of the IEEE EMBS, pp. 235-238, September 2004.
  8. J. H. Park, "Fusion of brain and IT," Journal of the IEEK, Vol. 37, No. 5, pp. 95-102, May 2010.
  9. B. Blankertz, F. Losch, M. Krauledat, G. Dornhege, G. Curio, K. R. Muller, "The Berlin Brain-Computer Interface: Accurate Performance From First-Session in BCI-Naive Subjects," Biomedical Engineering, IEEE Transactions on, Vol. 55, No. 10, pp. 2452-2462, October 2008. https://doi.org/10.1109/TBME.2008.923152
  10. H. G. Yeom, J. S. Kim, C. K. Chung, "Estimation of the velocity and trajectory of three-dimensional reaching movements from non-invasive magnetoencephalography signals," J Neural Eng. Vol. 10, No 2, February 2013.
  11. TW Eum, E-S Kim, "EEG-based brain computer interface technology," J. of the Korean Institute of Information Scientists and Engineers, Vol. 22, No. 2, pp 5-19, Feb 2004.
  12. W. Klimesch, "EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis," Brain Research Reviews, vol.29, issue.2-3, pp. 169-195. Elsevier, 1999. https://doi.org/10.1016/S0165-0173(98)00056-3
  13. M. Vazquez Marrufo, E. Vaquero, M. J. Cardoso, C. M. Gomez, "Temporal evolution of alpha and beta bands during visual spatial attention," In Cognitive Brain Research, vol.12, pp. 315-320. Elsevier, 2001. https://doi.org/10.1016/S0926-6410(01)00025-8
  14. Y. B. Koh, M. K. Ahn, W. B. Kim, S. C. Jun, "A Study of quantifying concentration index using electroencephalography," Journal of HCI Korea, pp. 23-25, Kangwon, Korea, January 2012.
  15. K. Yaomanee, S. Panngum, P. I. Na Ayuthaya, "Brain signal detection methodology for attention training using minimal EEG channels," in Proc. of IEEE Conf. on ICT & engineering, pp. 84-89, Bangkok, Thailand, November 2012.
  16. C. H. Lee, J. W. Kwon, G. D. Kim, J. E. Hong, D. S. Shin, D. H. Lee, "A Study on EEG based Concentration Transmission and Brain Computer Interface Application," Journal of the IEEK, Vol. 46, no. 2, pp. 41-46 , March 2009.

Cited by

  1. EEG Data Reliability Graph Generation Technique for Space-based VR Content Analysis vol.20, pp.1, 2019, https://doi.org/10.9728/dcs.2019.20.1.7