Wavelet Based Filters for Artifact Elimination in Electroencephalography Signal: A Review.

Area of Science:

• Neuroscience
• Signal Processing
• Biomedical Engineering

Background:

• Electroencephalography (EEG) records brain's electrical activity, with increasing research applications.
• Efficient EEG signal processing is crucial for improving data quality and research output.
• Traditional methods like Fourier Transform have limitations in analyzing transient EEG signals.

Purpose of the Study:

• To comprehensively review wavelet transform applications in denoising EEG signals.
• To describe various wavelet-based denoising methods used in recent EEG research.
• To discuss current challenges and recommend solutions in EEG wavelet-based signal processing.

Main Methods:

• Overview of basic EEG theory and wavelet transform principles.
• Description of common wavelet-based methods for EEG denoising.
• Review of recent scientific literature on wavelet applications in EEG analysis.

Main Results:

• Wavelet transform offers superior time-frequency localization and multi-resolution analysis for EEG.
• Wavelet methods efficiently extract transient information from EEG signals, outperforming Fourier Transform.
• A comprehensive overview of current wavelet-based EEG denoising techniques is presented.

Conclusions:

• Wavelet transforms are highly effective for denoising EEG signals, enhancing data quality.
• Further research is needed to address existing challenges in EEG wavelet-based processing.
• Recommended solutions aim to mitigate issues and advance the application of wavelets in EEG analysis.