Single-trial evoked potential estimation using wavelets.
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.
Wang
Zhisong
Z
School of Health Information Sciences, University of Texas Health Science Center at Houston, 7000 Fannin, Suite 600, Houston, TX 77030, USA.
Maier
Alexander
A
Leopold
David A
DA
Logothetis
Nikos K
NK
Liang
Hualou
H
D000465:Algorithms; D000818:Animals; D016477:Artifacts; D003198:Computer Simulation; D004569:Electroencephalography; D005074:Evoked Potentials, Visual / Q000502:physiology*; D008253:Macaca mulatta; D008297:Male; D008432:Mathematical Computing; D009039:Motion Perception / Q000502:physiology; D009474:Neurons / Q000502:physiology; D010364:Pattern Recognition, Visual; D011930:Reaction Time / Q000502:physiology; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012984:Software; D014793:Visual Cortex / Q000502:physiology*