EEG Eye Blink Artifacts Removal with Wavelet Denoising and Bandpass Filtering

Abstract

Many data sources can be analyzed using Wavelet Transforms (WT), a mathematical technique frequently used for extracting information from them. Although WT was effective at Blind Source Separation (BSS), it had some limitations, such as signal loss. The problem has been addressed with the introduction of a joint algorithm that combines WT with Frequency Domain Filtering (BPF). Wavelet Denoising Technique (WDT) and Band-Pass Filtering (BPF) are employed in this research to propose an innovative algorithm for combining advanced wavelet transform methods. Combining these two techniques helps reduce eye flutter in electroencephalograms (EEGs).FP1 signals produced by eye movement are filtered out by this novel algorithm. EEG signals should be captured dependably. Combined WTs perform better than traditional WTs, according to evidence. Based on Signal-to-Noise-Ration (SNR) and Power Spectral Density (PSD) measurements, the removal process has been demonstrated to be more efficient than a standard WT.