COMPRESSIVE DATA STORAGE FOR LONG-TERM EEG: VALIDATION BY VISUAL ANALYSIS
Dimension reduction is a significant goal in managing big biomedical data. Our results suggest a pathway for archival of meaningful representations of entire LTM datasets. The latent space suggests new lines of data-scientific inquiry of the EEG in acute neurological illness.
Authors
- Maria Bruzzone (ORCID: https://orcid.org/0000-0001-7552-1810)
- Giridhar P. Kalamangalam (ORCID: https://orcid.org/0000-0002-0178-4332)
- Jean E. Cibula
- Abbas Babajani‐Feremi (ORCID: https://orcid.org/0000-0001-8705-2855)
- K. Patel (ORCID: https://orcid.org/0000-0002-2756-0839)
- Sotiris Mitropanopoulos
- Carolina B. Maciel (ORCID: https://orcid.org/0000-0002-8763-5839)
- Subeikshanan Venkatesan (ORCID: https://orcid.org/0000-0001-5777-0530)
- Yue Wang (ORCID: https://orcid.org/0009-0005-8626-6620)
Institutions
- Cleveland Clinic (US)
- University of Florida Health (US)
- University of Florida (US)
- Mayo Clinic in Arizona (US)
Publication Details
- Journal
- Clinical Neurophysiology Practice
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.cnp.2025.07.005
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- University of Florida