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Active Segment Selection Method in EEG Classification Using Fractal Features

Authors: Samira Vafaye Eslahi

Abstract:

BCI (Brain Computer Interface) is a communication machine that translates brain massages to computer commands. These machines with the help of computer programs can recognize the tasks that are imagined. Feature extraction is an important stage of the process in EEG classification that can effect in accuracy and the computation time of processing the signals. In this study we process the signal in three steps of active segment selection, fractal feature extraction, and classification. One of the great challenges in BCI applications is to improve classification accuracy and computation time together. In this paper, we have used student’s 2D sample t-statistics on continuous wavelet transforms for active segment selection to reduce the computation time. In the next level, the features are extracted from some famous fractal dimension estimation of the signal. These fractal features are Katz and Higuchi. In the classification stage we used ANFIS (Adaptive Neuro-Fuzzy Inference System) classifier, FKNN (Fuzzy K-Nearest Neighbors), LDA (Linear Discriminate Analysis), and SVM (Support Vector Machines). We resulted that active segment selection method would reduce the computation time and Fractal dimension features with ANFIS analysis on selected active segments is the best among investigated methods in EEG classification.

Keywords: eeg, ANFIS, BCI, Student’s t- statistics, Fractal Features, FKNN

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1336048

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References:


[1] A. Kubler, and K. Muller, "an introduction to brain computer interfacing,” in: G.dornhege, J.D.R. Millan, T. Hinterberger, D.J. McFarland and K. Muller (Eds). Toward Brain-Computer Interfacing. The MIT Press Massachusetts. 2007; 1-25.
[2] J. R. Wolpaw, N Birbaumer, W. J. Heetderks, D. J. McFarland, P. H. Peckham, and G. Schalk, "Brain–computer interface technology: a review of the first international meeting,” IEEE Trans. Rehabil. Eng. 2000; 8(2):164–73.
[3] L. Parra, C. Alvino, A. C. Tang, B. A. Pearlmutter, N. Yeung, and A. Osman, "Linear spatial integration for single trial detection in encephalography. NeuroImage 2002; 7(1):223–30.
[4] X. Hu, D. V. Prokhorov, I. I. Wunsch, and C. Donald,” Time series prediction with a weighted bidirectional multi-stream extended Kalman filter,” Neurocomputing. 2007; 70: 2392–9.
[5] N. Stamatis, D. Parthimos, T. M. Griffith, "Forecasting chaotic cardiovascular time series with an adaptive slope multilayer perceptron neural network. IEEE Trans Biomed Eng 1999; 46(12):1441–53.
[6] H. J. Rong, N. Sundararajan, G. B. Huang, P. Saratchandran, "Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction,” Fuzzy Sets Syst 2006; 157 (9): 1260–75.
[7] J. S. Jang Roger, "ANFIS: adaptive-network-based fuzzy inference system,” IEEE Trans SMC 1993; 23(3):665–85.
[8] T. Higuchi, "Approach to an irregular time series on the basis of the fractal theory,” Physica D Nonlinear Phenomena. 1988; vol.31; 277-283.
[9] İ. Güler, E. D. Übeyli, "Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients,” Elsevier. Journal of Neuroscience Methods. 2005; 148(2):113-21.
[10] Q. Xu, H. Zhou, Y. Wang, J. Huang, " Fuzzy support vector machine for classification of EEG signals using wavelet-based features,” Elsevier, Medical Engineering & Physics. 2009; (31): 858- 865.
[11] K. Majumdar., R.M. Frank, "Three linear discriminators for separating scalp human EEG signals during RSVP tasks,” unpublished work, 2006.
[12] V. Bostanov, "BCI competition 2003 – data sets Ib and IIb: feature extraction from evenet related brain potentials with the continuous wavelet transform and the t-value scalogram,” IEEE Trans. Biomed. Eng. 2004, 1057–1061.
[13] S. Sun, C. Zhang, Y. Lu, "The random electrode selection ensemble for EEG signal classification,” Pattern Recogn., 2008; 14(5): 1663–1675.
[14] B. Blankertz, G. Curio, K. Muller, "Classifying Single Trial EEG: Towards Brain Computer Interfacing,” Advances in Neural Information Processing Systems (NIPS), MIT Press, Cambridge, MA, 2002, pp.157–164.
[15] F. Lotte, M. Congedo, A. Lecuyer, F. Lamarche, B. Arnaldi, "A review of classification algorithms for EEG-based brain-computer interface,” J. Neural Eng. 2007; 4 (2): R1–R13.
[16] U. Güçlü, Y. Güçlütürk, C. Loo, ”Evaluation of fractal dimension estimation methods for feature extraction in motor imagery based brain computer interface,” Elsevier, Procedia Computer Science. 2011; 3: 589–594.
[17] I. Daubechies, "Ten lectures on wavelets,” CBMS-NSFLecture Notes nr. 61, SIAM, 1992.
[18] W. Y. Hsu, C. C. Lin, M. S. Ju, Y. N. Sun, "Wavelet-based fractal features with active segment selection: Application to single-trial EEG data,” Journal of Neuroscience Methods. 2007; 163(1): 145–160.
[19] M. F. Barnsley, "Fractals Everywhere,” 2nd Ed. New York: Academic Press Professional; 1993.
[20] K. Falconer, "Fractal geometry: Mathematical foundations and applications,” Wiley, New York. 1990.
[21] J. Katz, "Fractals and the analysis of waveform,” Computers in Biology and Medicine. 1988; 18: 145-156.
[22] J. M. Keller, M. R. Gray, J. A. Givens,”A fuzzy k-nearest neighbor algorithm,” IEEE Transactions on Systems, Man and Cybernetics. SMC-1985; 15(4), 580-585.
[23] D. Cohen, E. Halgren, "Magnetoencephalography (Neuromagnetism). Preprint from Encyclopedia of Neuroscience,” Elsevier, 2003; 3rd Ed.
[24] J. Usher, D. Campbell, J. Vohra, J. Cameron, "A fuzzy logic-controlled classifier for use in implantable cardioverter defibrillators,” Pace-Pacing Clin Electrophysiol. 1999; 22: 183–6.
[25] S. Y. Belal, A. F. G. Taktak, A. J. Nevill, S. A. Spencer, D. Roden, S. Bevan, "Automatic detection of distorted plethysmogram pulses in neonates and paediatric patients using an adaptive-network-based fuzzy inference system,” Artif Intell Med 2002; 24:149–65.
[26] I. Guler, E. D. Ubeyli, "Detection of ophthalmic artery stenosis by leastmean squares backpropagation neural network,” Comput Biol Med. 2003; 33(4):333–43.
[27] I. Guler, E. D Ubeyli, "Application of adaptive neuro-fuzzy inference system for detection of electrocardiographic changes in patients with with partial epilepsy using feature extraction. Expert Syst Appl. 2004; 27(3):323–30.
[28] Virant-Klun I, Virant J. Fuzzy logic alternative for analysis in the biomedical sciences. Comput Biomed Res. 1999; 32:305- 21.