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Wavelet packet decomposition of a new filter -based on underlying neural activity- for ERP classification
, Article 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07, Lyon, 23 August 2007 through 26 August 2007 ; 2007 , Pages 1876-1879 ; 05891019 (ISSN) ; 1424407885 (ISBN); 9781424407880 (ISBN) ; Shamsollahi, M. B ; Hashemi, M. R ; Rezazadeh, I ; Sharif University of Technology
2007
Abstract
This paper introduces a wavelet packet algorithm based on a new wavelet like filter created by a neural mass model in place of wavelet. The hypothesis is that the performance of an ERP based BCI system can be improved by choosing an optimal wavelet derived from underlying mechanism of ERPs. The wavelet packet transform has been chosen for its generalization in comparison to wavelet. We compared the performance of proposed algorithm with existing standard wavelets as Db4, Bior4.4 and Coif3 in wavelet packet platform. The results showed a lowest cross validation error for the new filter in classification of two different kinds of ERP datasets via a SVM classifier. © 2007 IEEE
Nonlinear analysis of anesthesia dynamics by fractal scaling exponent
, Article 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06, New York, NY, 30 August 2006 through 3 September 2006 ; 2006 , Pages 6225-6228 ; 05891019 (ISSN); 1424400325 (ISBN); 9781424400324 (ISBN) ; Rabiee, H. R ; Hashemi, M. R ; Taslimi, P ; Ghanbari, M ; Sharif University of Technology
2006
Abstract
The depth of anesthesia estimation has been one of the most research interests in the field of EEG signal processing in recent decades. In this paper we present a new methodology to quantify the depth of anesthesia by quantifying the dynamic fluctuation of the EEG signal. Extraction of useful information about the nonlinear dynamic of the brain during anesthesia has been proposed with the optimum Fractal Scaling Exponent. This optimum solution is based on the best box sizes in the Detrended Fluctuation Analysis (DFA) algorithm which have meaningful changes at different depth of anesthesia. The Fractal Scaling Exponent (FSE) Index as a new criterion has been proposed. The experimental results...
Dimensional characterization of anesthesia dynamic in reconstructed embedding space
, Article 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07, Lyon, 23 August 2007 through 26 August 2007 ; 2007 , Pages 6483-6486 ; 05891019 (ISSN) ; 1424407885 (ISBN); 9781424407880 (ISBN) ; Rabiee, H. R ; Hashemi, M. R ; Ghanbari, M ; Sharif University of Technology
2007
Abstract
The depth of anesthesia quantification has been one of the most research interests in the field of EEG signal processing and nonlinear dynamical analysis has emerged as a novel method for the study of complex systems in the past few decades. In this investigation we use the concept of nonlinear time series analysis techniques to reconstruct the attractor of anesthesia from EEG signal which have been obtained from different hypnotic states during surgery to give a characterization of the dimensional complexity of EEG by Correlation Dimension estimation. The dimension of the anesthesia strange attractor can be thought of as a measure of the degrees of freedom or the 'complexity' of the...