Mth Order FIR Filtering for EEG Denoising Using Adaptive Recursive Least Squares Algorithm

摘要

In EEG Electroencephalogram signals Artifacts records are originated due to various factors as line interference, EOG (electro-Oculogram) and ECG (electrocardiogram). These noise sources upsurge the striving in analyzing the EEG and to procurement clinical information. Therefore, specific filters design is obligatory to diminution of such artifacts in EEG records. This research work anticipated an adaptive filtering method for eradicating ocular artifacts from EEG records by performing. Mth Order FIR Filtering on Adaptive RLS algorithm. In this paper, the method accuracy is estimated by utilizing virtual data quantitatively and equated with the precision of the time-domain regression method. The outcomes suggest that EEG channels are frequency dependent for transfer of ocular signal. The proposed adaptive filtering technique is more precise for denoising of EEG signals.