Removal of eye blinking artifacts from EEG incorporating a new constrained BSS algorithm

Area of Science:

• Biomedical Engineering
• Neuroscience
• Signal Processing

Background:

• Eye muscle artifacts are common in electroencephalograms (EEG).
• Current methods often discard contaminated data segments, losing potentially valuable information.
• Artifacts like blinks and speech can mask important neurological signals.

Purpose of the Study:

• To develop a robust constrained blind source separation (CBSS) algorithm.
• To effectively remove eye muscle artifacts from EEG data.
• To preserve data segments that would otherwise be rejected.

Main Methods:

• Developed a constrained blind source separation (CBSS) algorithm.
• Utilized reference signals as a constraint within the CBSS framework.
• Transformed the constrained problem into an unconstrained one using nonlinear penalty functions.
• Modified the cost function using the natural gradient algorithm (NGA) with incorporated reference signals.

Main Results:

• The CBSS algorithm demonstrated effective removal of eye muscle artifacts.
• The developed method allows for the retention of data segments previously discarded due to artifacts.
• Preservation of potentially important information masked by artifacts.

Conclusions:

• The developed CBSS algorithm offers an effective solution for artifact removal in EEG.
• This technique enhances the usability of EEG data by minimizing information loss.
• The approach holds promise for improving the accuracy and completeness of EEG analysis.