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

Tópicos

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Resumo

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.

Resumo Limpo

classic approach extract eventrel potenti erp brain ensembl averag long latenc erp optim part due timedelay obtain respons part latenc amplitud erp compon like p variabl depend cognit function studi compar perform simplifi kalman filter slide window averag track dynam chang singl trial p comparison perform simul p data ad background nois consist simul real background eeg various input signal nois ratio method can appli track dynam chang simplifi kalman filter advantag slide window averag notabl better nois suppress optim faster chang latenc amplitud p compon consider higher robust toward suboptim set latter great import clinic set optim set determin

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