Structured noise champagne: an empirical Bayesian algorithm for electromagnetic brain imaging with structured noise
A key aspect of our algorithm is that we do not require any additional baseline measurements to estimate the noise covariance from the sensor data under scenarios such as resting state analysis, and other use cases wherein a noise or artifactual source occurs only in the active period but not in the baseline period (e.g., neuro-modulatory stimulation artifacts and speech movements).
Authors
- Ali Hashemi (ORCID: https://orcid.org/0000-0003-4957-4630)
- Kensuke Sekihara (ORCID: https://orcid.org/0000-0002-2048-3797)
- Ashish Raj (ORCID: https://orcid.org/0000-0003-2414-2444)
- Yijing Gao (ORCID: https://orcid.org/0000-0003-0874-4445)
- Stefan Haufe (ORCID: https://orcid.org/0000-0003-1470-9195)
- Sanjay Ghosh (ORCID: https://orcid.org/0000-0002-0474-5072)
- Chang Cai (ORCID: https://orcid.org/0000-0002-2967-0644)
- Srikantan S. Nagarajan
Institutions
- Indian Institute of Technology Kharagpur (IN)
- University of California, San Francisco (US)
- University of California System (US)
- Central China Normal University (CN)
- Japan Chemical Analysis Center (JP)
- Technische Universität Berlin (DE)
Publication Details
- Journal
- Frontiers in Human Neuroscience
- Published
- 2025-04-07
- DOI
- https://doi.org/10.3389/fnhum.2025.1386275
- Citations
- 3
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 4.29
Funders
- National Science Foundation
- National Natural Science Foundation of China
- Ministry of Education, India
- Central China Normal University
- National Institutes of Health