A comparative study between a simplified Kalman filter and Sliding Window Averaging for single trial dynamical estimation of event-related potentials

TY - JOUR

T1 - A comparative study between a simplified Kalman filter and Sliding Window Averaging for single trial dynamical estimation of event-related potentials

AU - Vedel-Larsen, Esben

AU - Fuglø, Jacob

AU - Channir, Fouad

AU - Thomsen, Carsten E

AU - Sørensen, Helge B D

N1 - Copyright © 2009 Elsevier Ireland Ltd. All rights reserved.

PY - 2010/9

Y1 - 2010/9

N2 - The classical approach for extracting event-related potentials (ERPs) from the brain is ensemble averaging. For long latency ERPs this is not optimal, partly due to the time-delay in obtaining a response and partly because the latency and amplitude for the ERP components, like the P300, are variable and depend on cognitive function. This study compares the performance of a simplified Kalman filter with Sliding Window Averaging in tracking dynamical changes in single trial P300. The comparison is performed on simulated P300 data with added background noise consisting of both simulated and real background EEG in various input signal to noise ratios. While both methods can be applied to track dynamical changes, the simplified Kalman filter has an advantage over the Sliding Window Averaging, most notable in a better noise suppression when both are optimized for faster changing latency and amplitude in the P300 component and in a considerably higher robustness towards suboptimal settings. The latter is of great importance in a clinical setting where the optimal setting cannot be determined.

AB - The classical approach for extracting event-related potentials (ERPs) from the brain is ensemble averaging. For long latency ERPs this is not optimal, partly due to the time-delay in obtaining a response and partly because the latency and amplitude for the ERP components, like the P300, are variable and depend on cognitive function. This study compares the performance of a simplified Kalman filter with Sliding Window Averaging in tracking dynamical changes in single trial P300. The comparison is performed on simulated P300 data with added background noise consisting of both simulated and real background EEG in various input signal to noise ratios. While both methods can be applied to track dynamical changes, the simplified Kalman filter has an advantage over the Sliding Window Averaging, most notable in a better noise suppression when both are optimized for faster changing latency and amplitude in the P300 component and in a considerably higher robustness towards suboptimal settings. The latter is of great importance in a clinical setting where the optimal setting cannot be determined.

U2 - 10.1016/j.cmpb.2009.12.006

DO - 10.1016/j.cmpb.2009.12.006

M3 - Journal article

C2 - 20227130

SN - 0169-2607

VL - 99

JO - Computer Methods and Programs in Biomedicine

JF - Computer Methods and Programs in Biomedicine

IS - 3

ER -