Technical Program

Paper Detail

Paper:SPTM-P6.2
Session:Non-Stationary Signal Analysis and Modeling
Time:Thursday, May 20, 09:30 - 11:30
Presentation: Poster
Topic: Signal Processing Theory and Methods: Non-stationary Signals & Time-Frequency Analysis
Title: A WAVELET-BASED APPROACH FOR THE EXTRACTION OF EVENT RELATED POTENTIALS FROM EEG
Authors: Mehrdad Fatourechi; University of British Columbia / Neil Squire Foundation 
 Steven G. Mason; Neil Squire Foundation 
 Gary E. Birch; University of British Columbia / Neil Squire Foundation 
 Rabab K. Ward; University of British Columbia 
Abstract: Event Related Potentials (ERPs) are of interest to many researchers seeking knowledge about the functions of the brain. ERPs are low-frequency events that are usually obscured in single trial analysis. To visualize these signals; most of the reliable solutions at the present time use the ensemble averages of many single trials. In this paper, a wavelet-based method called Statistical Coefficient Selection (SCS) is used for the extraction of ERPs from EEG signals. Unlike other wavelet-based denoising methods, the current method does not focus on the wavelet coefficients of the signal itself. Instead, it selects the coefficients based on the statistical study of trials from training data set. Simulation results show the superiority of the proposed SCS method in extracting ERPs in comparison with other filtering approaches.
 
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