| Paper Abstract and Keywords |
| Presentation |
2023-06-16 16:50
Artifact Reduction in the EEG Grasping-Type Discrimination Task using Independent Component Analysis (ICA) method Phan Hoang Huu Duc, Masayuki Fujiwara, Kosei Shibata, Hiroaki Wagatsuma (LSSE, Kyushu Institute of Technology) MBE2023-15 |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
This study examined the artifactual components rejection of Independent Component Analysis (ICA) in Electroencephalogram (EEG) data from three grasping motion classification tasks. An unavoidable issue of EEG is that the data recording mix activities of sources that often contain some large and distracting artifacts. In this study, we use the ICA method combined with visual inspection for removing the contamination. The result showed that although ICA provides high-intensity artifacts rejection, the algorithm may relatively reduce the brain signal of interest for EEG data reconstruction and time-frequency ERD/ERS analysis. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Electroencephalography (EEG) / grasping tasks / independent component analysis (ICA) / event-related desynchronization/synchronization (ERD/ERS) / / / / |
| Reference Info. |
IEICE Tech. Rep., vol. 123, no. 82, MBE2023-15, pp. 28-33, June 2023. |
| Paper # |
MBE2023-15 |
| Date of Issue |
2023-06-09 (MBE) |
| ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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| Download PDF |
MBE2023-15 |