Comparison of Basic Linear Filters in Extracting Auditory Evoked Potentials

Authors : Serap AYDIN

Pages : 111-123

View : 9 | Download : 7

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :In the present study, the performances of two well-known linear filtering techniques are compared for extraction of auditory Evoked Potential insert ignore into journalissuearticles values(EP); from a relatively small number of sweeps. Both experimental and simulated data are filtered by the two algorithms into two groups. Group A consists of Wiener filtering insert ignore into journalissuearticles values(WF); applications, where conventional WF and Coherence Weighted WF insert ignore into journalissuearticles values(CWWF);); have been assessed in combination with the Subspace Method insert ignore into journalissuearticles values(SM);. Group B consists of the well-known adaptive filtering algorithms Least Mean Square insert ignore into journalissuearticles values(LMS);, Recursive Least Square insert ignore into journalissuearticles values(RLS);, and one-step Kalman filtering insert ignore into journalissuearticles values(KF);. Both groups are tested with respect to signal-to-noise ratio insert ignore into journalissuearticles values(SNR); enhancement by comparing to the traditional ensemble averaging insert ignore into journalissuearticles values(EA);. We observed that KF is the best method among them. The application of the SM before filtering improves the performance of the LMS and the assessments of WF where the CWWF works better than the conventional WF in that case. In conclusion, most of the linear filters show definitely better performance compared to EA. KF effectively reduces the experimental time insert ignore into journalissuearticles values(to one-fourth of that required by EA);. The SM that has recently been revealed in EP estimation is found to be a meaningful pre-filter as it significantly reduces the noise level of EEG raw data.
Keywords : Adaptive filtering, Wiener filtering, auditory evoked potential, EEG