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| 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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