Juggler’s ASR: Unpacking the principles of artifact subspace reconstruction for revision toward extreme MoBI
The proposed ASR<sub>DBSCAN</sub> and ASR<sub>GEV</sub> methods handle motion-related artifacts better than the original ASR algorithm, enabling researchers to better extract brain activity during real-world motor tasks. These methods provide a practical advantage in processing EEG data from experiments involving high-intensity motor activities, advancing biomedical research capabilities.
Authors
- John R. Iversen (ORCID: https://orcid.org/0000-0002-4172-9848)
- Christian Kothe (ORCID: https://orcid.org/0009-0009-3751-1908)
- Makoto Miyakoshi (ORCID: https://orcid.org/0000-0002-4062-6679)
- Chi-Yuan Chang (ORCID: https://orcid.org/0000-0003-0843-755X)
- Hyeonseok Kim (ORCID: https://orcid.org/0000-0003-1706-5030)
Institutions
- Cincinnati Children's Hospital Medical Center (US)
- University of California San Diego (US)
- Intuit (United States) (US)
- University of Cincinnati Medical Center (US)
- McMaster University (CA)
Publication Details
- Journal
- Journal of Neuroscience Methods
- Published
- 2025-05-03
- DOI
- https://doi.org/10.1016/j.jneumeth.2025.110465
- Citations
- 15
- Primary Topic
- Nuclear Physics and Applications
- Type
- article
- Field-Weighted Citation Impact
- 28.24