An automatic electroencephalography blinking artefact detection and removal method based on template matching and...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Electroencephalogram (EEG) signals are frequently distorted by electrooculographic (EOG) artifacts.
• Blinking artifacts (BAs) are a common type of EOG interference, significantly impacting EEG data quality.

Purpose of the Study:

• To propose and evaluate a novel method for detecting and removing EOG Blinking Artifacts (BAs) from EEG signals.
• To improve the Signal-to-Error Ratio (SER) of EEG contaminated regions.

Main Methods:

• Detection of BAs using Normalized Correlation Coefficient (NCC) against a template library.
• Application of Ensemble Empirical Mode Decomposition (EEMD) to artifact-contaminated EEG segments.
• Statistical algorithm to identify and isolate Intrinsic Mode Functions (IMFs) representing BAs.

Main Results:

• The proposed method successfully detected and removed artificially generated EOG BAs from simulated EEG signals.
• Significant improvement in EEG signal quality was observed, with an average increase of 35 dB in the Signal-to-Error Ratio (SER) of the contaminated region (CR).

Conclusions:

• The developed method offers an effective solution for EOG BA removal in EEG.
• This technique has the potential to enhance the reliability and accuracy of EEG-based analyses.