Related Experiment Video
Updated: Mar 7, 2026
10:41
Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content
Published on: May 26, 2018
7.4K
A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis
Balbir Singh1, Hiroaki Wagatsuma2
1Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology (KYUTECH), Kitakyushu, Japan.
Computational and Mathematical Methods in Medicine
|February 15, 2017
Summary
This study introduces Morphological Component Analysis (MCA) to accurately decompose electroencephalogram (EEG) signals, separating ocular artifacts from brain activity. The method effectively identifies distinct EEG signal morphologies for improved analysis.
Area of Science:
- Neuroscience
- Signal Processing
- Biomedical Engineering
Background:
- Electroencephalogram (EEG) signals are often contaminated by ocular artifacts.
- Existing methods like PCA and ICA have limitations in artifact removal.
- Signal morphology is crucial for accurate EEG interpretation.
Purpose of the Study:
- To develop a systematic decomposition method for identifying signal components in EEG.
- To utilize Morphological Component Analysis (MCA) for artifact removal and signal separation.
- To determine the optimal dictionary combination for EEG signal decomposition.
Main Methods:
- Applied Morphological Component Analysis (MCA) based on time-frequency sparsity.
- Utilized multiple bases (dictionaries) for accurate signal reconstruction.
- Tested redundant transforms including UDWT, DCT, LDCT, DST, and DIRAC on real and semi-realistic iEEG data.
Main Results:
- MCA successfully decomposed EEG signals into distinct morphological components.
- Identified UDWT, DST, and DIRAC as the most suitable dictionary combination for EEG analysis.
- Demonstrated accurate decomposition of baseline envelope, multifrequency waveforms, and spiking activities.
Conclusions:
- Morphological Component Analysis (MCA) offers an effective approach for EEG signal decomposition and artifact removal.
- The UDWT, DST, and DIRAC combination provides accurate representation of key EEG signal morphologies.
- This method enhances the analysis of complex EEG data, improving diagnostic potential.
