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

The Institute of Electronics and Information Engineers (대한전자공학회)
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EEG Signal Classification Algorithm based on DWT and SVM for Driving Robot Control

주행로봇제어를 위한 DWT와 SVM기반의 EEG신호 분류 알고리즘

  • 이기배 (제주대학교 해양시스템공학과) ;
  • 이종현 (제주대학교 해양시스템공학과) ;
  • 배진호 (제주대학교 해양시스템공학과) ;
  • 이재일 (제주대학교 해양시스템공학과)
  • Received : 2015.03.11
  • Accepted : 2015.07.28
  • Published : 2015.08.25
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Abstract

In this paper, we propose a classification algorithm based on the obtained EEG(Electroencephalogram) signal for the control of 'left' and 'right' turnings of which a driving system composed of EEG sensor, Labview, DAQ, Matlab and driving robot. The proposed algorithm uses features extracted from frequency band information obtained by DWT (Discrete Wavelet Transform) and selects features of high discrimination by using Fisher score. We, also propose the number of feature vectors for the best classification performance by using SVM(Support Vector Machine) classifier and propose a decision pending algorithm based on MLD (Maximum Likelihood Decision) to prevent malfunction due to misclassification. The selected four feature vectors for the proposed algorithm are the mean of absolute value of voltage and the standard deviation of d5(2-4Hz) and d2(16-32Hz) frequency bands of P8 channel according to the international standard electrode placement method. By using the SVM classifier, we obtained 98.75% accuracy and 1.25% error rate. Also, when we specify error probability of 70% for decision pending, we obtained 95.63% accuracy and 0% error rate by using the proposed decision pending algorithm.

본 논문은 '좌', '우' 방향 제어를 위해 취득된 EEG(Electroencephalogram) 신호 기반 분류 알고리즘과 EEG 센서, Labview, DAQ, Matlab, 주행로봇으로 구성된 방향 제어 시스템을 제안한다. 제안된 알고리즘은 DWT(Discrete Wavelet Transform)로 추출된 주파수대역 정보를 특징으로 이용하며, Fishers score를 이용하여 변별력이 높은 주파수 대역의 특징을 선별한다. 또한, SVM (Support Vector Machine)을 이용하여 분류 성능이 최고가 되는 특징벡터의 조합을 제안하고, 잘못된 판정에 의한 오동작을 방지하기 위한 MLD(Maximum Likelihood Decision) 기반의 판정보류 알고리즘도 제안한다. 제안된 알고리즘에 의해 선택된 4개의 특징벡터는 국제 표준 전극 배치법에 따른 P8 채널의 d2(16-32Hz), d5(2-4Hz) 주파수 대역의 전압의 절대 값 평균과 표준편차이다. SVM 분류기로 실험한 결과 98.75%의 정확도와 1.25%의 오류율 성능을 보였다. 또한, 오류 확률 70%를 판정 보류로 규정할 경우, 제안된 알고리즘은 인식률 95.63%의 정확도와 오류율 0%을 보였다.

Keywords

References

  1. M. Congedo, F. Lotte and A. Lecuyer, "Classific ation of movement intention by spatially filtered electromagnetic inverse solutions", Physics in Medicine and Biology, Vol. 51, No. 8, pp. 1971-198 9, April, 2006 https://doi.org/10.1088/0031-9155/51/8/002
  2. Wenjie Xu, Cuntai Guan, Chng Eng Siong, S.R angantha, M. Thulasidas and Jiankang Wu, "High Accuracy Classification of EEG signal", In 17th International Conference on Pattern Recognition (ICPR'04), Vol. 2, pp.391-394, August, 2004
  3. F. Galan, M. Nuttin, E. Lew, P. W. Ferrez, G. Vanacker, J. Philips, J. del R. Millan. "A brain-actuated wheelchair: Asynchronous and non-invasive Brain-computer interfaces for continuous control of robots", Clinical Neurophysiology, Vol. 119, No. 9, pp. 2159-2169, June, 2008 https://doi.org/10.1016/j.clinph.2008.06.001
  4. Luca Tonin, Robert Leeb, Michele Tavella, Serafeim Perdikis, Josedel R. Millan, "The role of shared-control in BCI-based telepresence", 2010 IEEE International Conference on System Manand Cybernetics, pp.1462-1466, October, 2010
  5. Myeong-Chun Lee, Sung-Bae Cho, "Brain-Computer Interface Implementation for Controling Electroemcephalograph Based 3D Virtual Car Simulator", KCC Fall Conference, Vol. 39, No. 2(B), pp. 280-282, November, 2012
  6. Hong Kee Kim, Ki Hong Kim, Jong Sung Kim, Wook Ho Son, "A Control method of Left-Right directions by analyzing EEG Signals", HCI 2006, pp. 1005-1010, February, 2006
  7. Seung Hoon Lee, Dong Han Yoon, Introduction to the Wavelet Transform, Jinhan Books, 2003
  8. Jaeil Lee, Youn Joung Kang, Chong Hyun Lee, Seung Woo Lee and Jinho Bae, "Analysis of Fea tures and Discriminability of Transient Signals for a Shallow Water Ambient Noise Environment", Journal of the Institute of Electronics and Information Engineers, Vol. 51, No. 7, pp. 209-220, July, 2013 https://doi.org/10.5573/ieie.2014.51.7.209
  9. Hag Yong Han, Introduction to Pattern Recognition, Hanbit media, 2009
  10. Hun jun Yang, Kyung Bo Hong and Dong Seok Jeong, "Road Surface Condition detect unsing Wavelet transform and SVM Classifier", in Proc. of IEEK autumn Conf., pp. 592-595, Seoul, Korean, November, 2012
  11. Emotiv Systems, Emotiv - brain computer interface technology, http://emotiv.com
  12. Seung Ho Lee, "Meditation and EEG", Journal of Korean Institute of Brain Science, Vol. 50, pp. 32-39, January, 2015
  13. D. Garrett, D. A. Peterson, C. W. Anderson, M. H. Thaut, "Comparison of linear, nonlinear, and feature selection methods for eeg signal classification" IEEE Transactions on Neural System and Rehabilitation Engineering, Vol. 11, No. 2, pp. 141-144, June, 2003 https://doi.org/10.1109/TNSRE.2003.814441
  14. G. N. Garcia, T. Ebrahimi, J. M. Vesin, "Support vector eeg classification in the fourier and time-frequency correlation domains", In Conference Proceedings of the First International IEEE EMBS Conference on Neural Engineering, pp. 591-594, March, 2003
  15. B. Blankertz, G. Curio, K. R Muller, "Classifying single trial eeg: Towards brain computer interfacing" Advances in Neural Information Processing Systems(NIPS01), Vol. 14, pp. 11-22, 2004.
  16. Youn Joung Kang, Jaeil Lee, Jinho Bae and Chong Hyun Lee, "Target Classification Algorithm Using Complex-valued Support Vector Machine", Journal of the Institute of Electronics and Information Engineers, Vol. 50, No. 4, pp. 182-188, April 2013 https://doi.org/10.5573/ieek.2013.50.4.182
  17. Makeblock, Starter Robot kit V2.0, http://www.makeblovk.cc.
  18. National instruments, NI myDAQ Specifications, http://digital.ni.com.

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