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DENOISING OF AUDITORY EVOKED POTENTIALS

Thepurpose of this study is to reduce the number of sweeps in extracting the auditory evoked potential (AEP) from ongoing electroencephalography (EEG) to minimize the time of diagnosis. In noisy environments, when conventional ensemble averaging of waveforms is used, recording must continue for excessively long time in order to accurately detect the ABR signal. This leads to problem when subject under test is infants, children, or others who may not be cooperative. Only partial data may be collected and a follow on appointment must be arranged to complete the test adding to costs and inconvenience for all concerned. So using basic estimation techniques in estimating the AEP with less number of sweeps compared to traditional Ensemble average (EA) is aim of this study. Signal to noise ratio (SNR) is parameter used to compare performance of different filter outputs against EA. For this purpose, basic estimation techniques are categorized into two groups. Group A includes Wiener filter (WF), combination of subspace method (SM) and wiener filter, Coherence weighted wiener filter (CWWF).Group B consists of standard adaptive filtering techniques like Least mean square (LMS), Recursive least square (RLS),one-step Kalman filter. Performance of above techniques is checked for both simulated and experimental data. In simulation Gaussian noise is added to known template AEP to create noisy sweeps. Keywords—Auditory Evoked Potential, Adaptive filtering, Subspace method, EEG