Browse > Article
http://dx.doi.org/10.5573/ieie.2015.52.8.117

EEG Signal Classification Algorithm based on DWT and SVM for Driving Robot Control  

Lee, Kibae (Jeju Natioanl University)
Lee, Chong Hyun (Jeju Natioanl University)
Bae, Jinho (Jeju Natioanl University)
Lee, Jaeil (Jeju Natioanl University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.52, no.8, 2015 , pp. 117-125 More about this Journal
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.
Keywords
EEG signal; DWT; SVM; Fisher score; MLD;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
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   DOI   ScienceOn
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   DOI   ScienceOn
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   DOI
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   DOI   ScienceOn
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   DOI   ScienceOn
17 Makeblock, Starter Robot kit V2.0, http://www.makeblovk.cc.
18 National instruments, NI myDAQ Specifications, http://digital.ni.com.