Methods for artifact detection and removal from scalp EEG: A review

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Electroencephalography (EEG) is a primary tool for recording brain activity.
• Artifacts from non-brain sources significantly contaminate EEG signals.
• Effective artifact detection and removal are crucial for reliable EEG analysis.

Purpose of the Study:

• To provide a comprehensive review of state-of-the-art artifact detection and removal methods for scalp EEG.
• To analyze the advantages and disadvantages of existing artifact handling techniques.
• To guide future research in developing novel or improved artifact management algorithms.

Main Methods:

• Review of existing literature on EEG artifact detection and removal.
• Categorization of common artifact types and their impact on EEG applications.
• Functional comparison of artifact removal methods based on artifact type and application suitability.

Main Results:

• No single artifact detection/removal method is universally effective.
• Different methods offer varying degrees of success for specific artifact types and applications.
• The field of EEG artifact handling remains an active area of research.

Conclusions:

• A thorough understanding of artifact types and current methods is essential for researchers.
• Future research should focus on developing more complete and universal artifact handling solutions.
• This review serves as a valuable resource for researchers and developers in the EEG field.