Low Complexity Automatic Stationary Wavelet Transform for Elimination of Eye Blinks from EEG

Area of Science:

• Neuroscience
• Signal Processing
• Biomedical Engineering

Background:

• Electroencephalogram (EEG) signals are crucial for brain activity analysis but are often contaminated by artifacts.
• Eye blink artifacts significantly impact EEG data quality due to their high amplitude.
• Effective artifact removal is essential for accurate EEG interpretation.

Purpose of the Study:

• To propose a low-complexity method for removing eye blink artifacts from EEG signals.
• To evaluate the proposed method's performance against established techniques like AWICA.
• To demonstrate the suitability of the method for real-time EEG processing.

Main Methods:

• A novel approach combining Stationary Wavelet Transform (SWT) and skewness was developed.
• The proposed method was compared with Automatic Wavelet Independent Components Analysis (AWICA) and Enhanced AWICA.
• Performance was quantified using Normalized Root Mean Square Error (NRMSE), Peak Signal-to-Noise Ratio (PSNR), and correlation coefficient (ρ).

Main Results:

• The proposed SWT and skewness method achieved superior artifact removal compared to AWICA and Enhanced AWICA.
• The method demonstrated lower NRMSE, higher PSNR, and a higher correlation coefficient.
• Significantly faster execution speed was observed, indicating real-time processing potential.

Conclusions:

• The proposed low-complexity method effectively removes eye blink artifacts from EEG signals.
• This technique offers improved performance and speed over existing methods.
• Its efficiency makes it highly suitable for real-time applications in EEG analysis.