A new regression-based method for the eye blinks artifacts correction in the EEG signal, without using any EOG...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Eye blinks are common artifacts in electroencephalography (EEG) signals, necessitating effective correction methods for accurate analysis.
• Current methods like regression-based techniques and Independent Component Analysis (ICA) have limitations, including data requirements and computational load.
• The optimal method for EEG artifact correction remains application-dependent due to varying performance and constraints.

Purpose of the Study:

• To introduce and evaluate a new regression-based algorithm for correcting eye blink artifacts in EEG.
• To compare the proposed method against established algorithms: Gratton, extended InfoMax, and SOBI.
• To assess the efficiency, data requirements, and signal preservation capabilities of the new method.

Main Methods:

• Development of a novel regression-based algorithm for eye blink artifact removal from EEG.
• Comparative analysis using EEG data contaminated with eye blinks.
• Evaluation against three widely used artifact correction algorithms: Gratton, extended InfoMax, and SOBI.

Main Results:

• The proposed regression-based method demonstrated comparable efficiency to existing techniques in correcting eye blinks.
• The new algorithm does not require electrooculography (EOG) channels, a large number of electrodes, or significant computational resources.
• The method successfully preserved crucial EEG information in segments free of eye blinks.

Conclusions:

• The novel regression-based approach offers an efficient and less demanding alternative for EEG eye blink artifact correction.
• This method provides a practical solution by minimizing data and computational requirements while maintaining signal quality.
• The proposed algorithm is suitable for various applications where EEG artifact removal is critical.