EEG Signal De-Noising Based on the Fejer-Korovkin Wavelet Filter

Contenu principal de l'article

B. V. V. S. R. K. K. Pavan, Dr. P. Esther Rani


The procedure used to measure electrical activity in the brain is an electroencephalogram (EEG). Via electrical impulses, brain cells interact with each other. The EEG can be used to help identify possible issues associated with this operation.Processing of the EEG signals in a noisy environment is a major problem in biomedical signal processing. Especially, Electroencephalography (EEG) acquisition and processing is crucial and difficult concept to step forward.Many neurogenic sounds and non-neurogenic interferences follow the actual EEG signal. The calculated brain response is an event-related potential (ERP) that is the direct product of a sensory, cognitive, or motor event which is low.The challenging task is that rebuilding of ERP signal fromcontaminated EEG signal. In this article, we propose an efficient approach that combines the decomposition of the empirical mode of the ensemble (EEMD) and fejer-korovkin filtering. The proposed algorithm began with the decomposition of noisy signals Using EEMD Using the comparison between the decomposed IMFs and the original signalseveral intrinsic mode function (IMF) components are obtained. Then by using fejer-korovkin filtering IMF components are de-noised. From these de-noised IMFsrequired ERP signal is reassembled. The suggested algorithm is tested using real EEG signals and compared with existing methods of Hybrid Mother Wavelet Using DWT and EMD-IIT with the Performance parameters of Standard deviation, Peak signal to noise ratio (PSNR), Pearson correlation coefficient(PCC) and Root mean square error (RMSE).

Renseignements sur l'article