Single-trial evoked potential estimation using wavelets
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, lea...
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| Published in | Computers in biology and medicine Vol. 37; no. 4; pp. 463 - 473 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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United States
Elsevier Ltd
01.04.2007
Elsevier Limited |
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| Abstract | 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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| AbstractList | 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. 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. |
| Author | Leopold, David A Logothetis, Nikos K Maier, Alexander Wang, Zhisong Liang, Hualou |
| Author_xml | – sequence: 1 fullname: Wang, Zhisong – sequence: 2 fullname: Maier, Alexander – sequence: 3 fullname: Leopold, David A – sequence: 4 fullname: Logothetis, Nikos K – sequence: 5 fullname: Liang, Hualou |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/16987507$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/s00422-003-0433-7 10.1016/S1388-2457(02)00365-6 10.1109/10.335844 10.1007/978-1-4612-2544-7_9 10.1109/18.382009 10.1109/TBME.1983.325187 10.1007/BF00201024 10.1016/S1388-2457(99)00011-5 10.1093/biomet/81.3.425 10.1109/10.277274 10.1111/1467-9868.00071 |
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| Keywords | Wavelet Single-trial LMS Wiener Evoked potential Structure-from-motion (SFM) RLS Translation-invariant Local field potential (LFP) Multiunit activity (MUA) |
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