Title :
A fuzzy logic based approach for intelligent extraction of brain evoked potentials
Author :
Zhang, Jain-Hua ; Wang, Xing-Yu
Author_Institution :
Dept. of Autom., East China Univ. of Sci. & Technol., Shanghai
Abstract :
A self-learning fuzzy system design and modeling approach based on TSK fuzzy model is proposed in this paper. On the basis of the input-output training data, the nonlinear (membership function) and linear (weighting coefficient) parameters in the IF and THEN part of fuzzy rules were separately optimized by supervised Gaussian learning and steady-state Kalman filter, respectively. Finally, the proposed approach was successfully applied to visual evoked potential (VEP) extraction.
Keywords :
Kalman filters; fuzzy logic; fuzzy systems; medical signal processing; unsupervised learning; visual evoked potentials; TSK fuzzy model; brain evoked potential; fuzzy logic based approach; fuzzy rule; input-output training data; intelligent extraction; self-learning fuzzy system design; steady-state Kalman filter; supervised Gaussian learning; visual evoked potential extraction; Brain modeling; Data mining; Design automation; Electronic mail; Filtering; Fuzzy logic; Fuzzy systems; Kalman filters; Steady-state; Training data; Fuzzy modeling; Kalman filtering; Supervised Gaussian learning; VEP signal extraction; hybrid learning algorithm;
Conference_Titel :
Control and Decision Conference, 2008. CCDC 2008. Chinese
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-1733-9
Electronic_ISBN :
978-1-4244-1734-6
DOI :
10.1109/CCDC.2008.4597987
