Temporal state-space model for forecasting slow-wave EEG power in non-human primates
This study demonstrated the TBATS model as a robust and efficient tool for forecasting longitudinal EEG signals. By accurately modeling subject-specific temporal structure with minimal data requirements, this approach provided a practical method for establishing a personalized neural baseline, critical for tracking neurodegeneration or optimizing closed-loop therapies.
Authors
- Maxwell D. Murphy (ORCID: https://orcid.org/0000-0002-8559-2361)
- Darcy M. Griffin (ORCID: https://orcid.org/0000-0001-5132-5073)
- Julian Low
- Douglas J Weber
- Ruitong Jiang (ORCID: https://orcid.org/0000-0001-9849-0500)
Institutions
- Center for the Neural Basis of Cognition (US)
- Carnegie Mellon University (US)
Publication Details
- Journal
- Journal of Neural Engineering
- Published
- 2026-04-21
- DOI
- https://doi.org/10.1088/1741-2552/ae62a9
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Defense Advanced Research Projects Agency