Work

Signal Decomposition and Noise Reduction in Single-Channel EEG: A Morphological Component Analysis (MCA) Approach

Year: 2025

Type: conference-paper

Abstract: 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.

Source: Proceedings of International Conference on Artificial Life and Robotics

DOI: 10.5954/icarob.2025.gs6-1

Authors: Kosei Shibata, Yide Yang, Rena Kato, Hendry Ferreira Chame, Laurent Bougrain, Hiroaki Wagatsuma

Institutions: Kyushu Institute of Technology, Université de Lorraine, Sorbonne Université

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: bronze

Funders: Université de Lorraine, Kyushu Institute of Technology, Japan Society for the Promotion of Science

Awards: G4839460500, Design science for body kinetics enhancement devices to realize desired functions by an integration of 3D analysis and inverse dynamics