An adaptive algorithm for single-trial P300 evoked potential extraction based on dynamic feature library

Abstract

Objective The single-trial extraction method of evoked potential has been one of the problems in EEG information processing field. According to the characteristics of somatosensory evoked electroencephalogram (EEG) with low signal-to-noise ratio and large parameter variation between trials, a novel single-trial extraction method for evoked potentials was proposed. This method aims to further improve the accuracy and characteristics of the single-trial extraction algorithm, preserve more dynamic characteristics between trials, and improve the estimation accuracy. Methods Based on wavelet filtering and multiple linear analysis, a new single-trial extraction method for EEG P300 parameters was proposed by applying the adaptive dynamic feature library. Four groups of wavelet filtered evoked EEG data were randomly selected, and used to build the feature library using overlapping average method and principal component analysis. For the single-trial extracted EEG data, the component with the highest correlation coefficient related with the current data was selected as the independent variable from the feature library, and the relevant multiple linear regression analysis was conducted. The single-trial evoked potential signal was reconstructed by the regression analysis results, in which the key features such as latency and amplitude were automatically extracted. Results Compared with the benchmark values determined by experts, the proposed algorithm can obtain more accurate estimation values of latency and amplitude in P300 components. The average difference of latency and amplitude by the proposed algorithm is (11.16±8.60) ms and (1.40±1.34) μV, respectively. These two values obtained by the proposed algorithm are much closer to that obtained by the commonly used overlapping average method of (23.26 ± 25.76) ms and (2.52 ± 2.50) μV, respectively. These results show that the proposed algorithm has significant advantages comparing with the traditional multiple linear regression analysis algorithm. Conclusions The dynamic updating principal component sample library of EEG data was applied to wavelet filtering and multiple linear regression, thus the dynamic characteristics were effectively preserved, and the accuracy of parameter estimation was improved. Key words: Multiple linear regression with dispersion terms; Evoked electroencephalogram; P300; Principal component analysis