E Vannier1, A Naït-Ali. 1. CETP, 10-12 avenue de l'Europe 78140 Vélizy, France.
Abstract
OBJECTIVE: The present paper introduces a new perspective on the classical ensemble averaging which can be useful to analyse the Brainstem Auditory Evoked Potentials (BAEPs). The analysis of the dynamics, related to the BAEP, is performed directly after its acquisition from the electroencephalogram (EEG). METHODS: The method primarily consists of dynamically modelling the averaged potential, obtained during the acquisition mode. Each averaging of signal at a given instant is considered as an autoregressive (AR) process. RESULTS: It has been shown that the predicting error power of AR modelling can be useful to provide an efficient tool to analyse the BAEPs. It has also been shown that the method is capable of taking the non-stationarities of both the BAEP and the EEG into account. CONCLUSION: In order to validate our approach, the proposed technique has been implemented for both simulated and real signals. This approach can also be employed in the context of estimating other evoked potentials and shows rich promise for potential clinical applications in future.
OBJECTIVE: The present paper introduces a new perspective on the classical ensemble averaging which can be useful to analyse the Brainstem Auditory Evoked Potentials (BAEPs). The analysis of the dynamics, related to the BAEP, is performed directly after its acquisition from the electroencephalogram (EEG). METHODS: The method primarily consists of dynamically modelling the averaged potential, obtained during the acquisition mode. Each averaging of signal at a given instant is considered as an autoregressive (AR) process. RESULTS: It has been shown that the predicting error power of AR modelling can be useful to provide an efficient tool to analyse the BAEPs. It has also been shown that the method is capable of taking the non-stationarities of both the BAEP and the EEG into account. CONCLUSION: In order to validate our approach, the proposed technique has been implemented for both simulated and real signals. This approach can also be employed in the context of estimating other evoked potentials and shows rich promise for potential clinical applications in future.