Signal Decomposition and Noise Reduction in Single-Channel EEG: A Morphological Component Analysis (MCA) Approach
This study applies the Morphological Component Analysis (MCA) to single-channel EEG data obtained during human-to-human interactions in a board game (Hex-game).MCA, a dictionary-based signal decomposition method, separates signals into distinct morphological components.It enables the extraction of plausible brain activity and the removal of noise, such as ocular and muscular artifacts.By focusing on neural dynamics in interactive settings, this approach highlights the relationship between cognitive processes and social behavior.The approach suggests that MCA offers a promising framework for EEG analysis in complex, dynamic environments, combining effective feature extraction with robust artifact removal.
Authors
- Hiroaki Wagatsuma (ORCID: https://orcid.org/0000-0001-5581-5942)
- Yuanmu Yang (ORCID: https://orcid.org/0000-0002-5264-0822)
- Hendry Ferreira Chame (ORCID: https://orcid.org/0000-0002-6293-8198)
- Rena Kato
- Laurent Bougrain (ORCID: https://orcid.org/0000-0001-6794-0505)
- Kosei Shibata
Institutions
- Kyushu Institute of Technology (JP)
- Sorbonne Université (FR)
- Laboratoire Médiations (FR)
- Université de Lorraine (FR)
Publication Details
- Journal
- Proceedings of International Conference on Artificial Life and Robotics
- Published
- 2025-02-13
- DOI
- https://doi.org/10.5954/icarob.2025.gs6-1
- Primary Topic
- Neural Networks and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Université de Lorraine
- Kyushu Institute of Technology
- Japan Society for the Promotion of Science