Journal article
Single-trial evoked potential estimation using wavelets
Computers in biology and medicine, v 37(4), pp 463-473
2007
PMID: 16987507
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In this paper we present conventional and translation-invariant (TI) wavelet-based approaches for single-trial evoked potential estimation based on intracortical recordings. We demonstrate that the wavelet-based approaches outperform several existing methods including the Wiener filter, least mean square (LMS), and recursive least squares (RLS), and that the TI wavelet-based estimates have higher SNR and lower RMSE than the conventional wavelet-based estimates. We also show that multichannel averaging significantly improves the evoked potential estimation, especially for the wavelet-based approaches. The excellent performances of the wavelet-based approaches for extracting evoked potentials are demonstrated via examples using simulated and experimental data.
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Details
- Title
- Single-trial evoked potential estimation using wavelets
- Creators
- Zhisong Wang - School of Health Information Sciences, University of Texas Health Science Center at Houston, 7000 Fannin, Suite 600, Houston, TX 77030, USAAlexander Maier - Unit on Cognitive Neurophysiology and Imaging, National Institute of Health, Building 49, Room B2J-45, MSC-4400, 49 Convent Dr., Bethesda, MD 20892, USADavid A Leopold - Unit on Cognitive Neurophysiology and Imaging, National Institute of Health, Building 49, Room B2J-45, MSC-4400, 49 Convent Dr., Bethesda, MD 20892, USANikos K Logothetis - Max Planck Institut für biologische Kybernetik, Spemannstrasse 38, 72076 Tübingen, GermanyHualou Liang - School of Health Information Sciences, University of Texas Health Science Center at Houston, 7000 Fannin, Suite 600, Houston, TX 77030, USA
- Publication Details
- Computers in biology and medicine, v 37(4), pp 463-473
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000245156200004
- Scopus ID
- 2-s2.0-33846485096
- Other Identifier
- 991014877777904721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biology
- Computer Science, Interdisciplinary Applications
- Engineering, Biomedical
- Mathematical & Computational Biology