Kalman Filter Based Estimation of Auditory Steady State Response Parameters

Luke, Robert H.; Wouters, Jan

doi:10.1109/tnsre.2016.2551302

Cited by 27 publications

(22 citation statements)

References 24 publications

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“…Although several artifact removal methods have been reported in the literature to record eASSRs free from stimulation artifacts in CI users [24][25][26][27] , only linear interpolation between artifact free samples before and after the stimulation artifact ( Fig. 1A) has resulted in consistent success 13,19,22,28,29 .…”

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confidence: 99%

Electrophysiological assessment of temporal envelope processing in cochlear implant users

Gransier

Carlyon

Wouters

2020

Sci Rep

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Cochlear-implant (CI) users rely on temporal envelope modulations (TEMs) to understand speech, and clinical outcomes depend on the accuracy with which these TEMs are encoded by the electrically-stimulated neural ensembles. Non-invasive EEG measures of this encoding could help clinicians identify and disable electrodes that evoke poor neural responses so as to improve CI outcomes. However, recording EEG during CI stimulation reveals huge stimulation artifacts that are up to orders of magnitude larger than the neural response. Here we used a custom-built EEG system having an exceptionally high sample rate to accurately measure the artefact, which we then removed using linear interpolation so as to reveal the neural response during continuous electrical stimulation. In ten adult CI users, we measured the 40-Hz electrically evoked auditory steady-state response (eASSR) and electrically evoked auditory change complex (eACC) to amplitude-modulated 900-pulses-per-second pulse trains, stimulated in monopolar mode (i.e. the clinical default), and at different modulation depths. We successfully measured artifact-free 40-Hz eASSRs and eACCs. Moreover, we found that the 40-Hz eASSR, in contrast to the eACC, showed substantial responses even at shallow modulation depths. We argue that the 40-Hz eASSR is a clinically feasible objective measure to assess TEM encoding in CI users.

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confidence: 99%

Electrophysiological assessment of temporal envelope processing in cochlear implant users

Gransier

Carlyon

Wouters

2020

Sci Rep

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“…In order to ensure the accuracy of the KF, and must be set accurately. However, the initial value is usually set by experience or imagination, or assumes it is known, so as to avoid setting [25][26][27]. Therefore, it is necessary consider whether the size of initial values will affect the KF state in the LDS.…”

Section: A Difficult To Determine Initial Valuesmentioning

confidence: 99%

Open Problems in Applications of the Kalman Filtering Algorithm

Song¹,

Shou-song²

2019

Proceedings of the 2019 International Conference on Mathematics, Big Data Analysis and Simulation and Modelling (MBDASM 2019)

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In practical application, the Kalman filter (KF) still have technical problems which have not been solved in the LDS, such as the determination of filter initial values, the slight deviation of model coefficients, the outlier or systematic deviation of measurement data and the covariance estimation of model disturbance and measurement errors. Whether the above situations affect the KF estimation and its accuracy, it is a practical problem which is high-profile and unavoidable in the application of the KF. Therefore, in this paper, take a typical linear state-space model as object, Monte Carlo method is used to simulate and verify the above technical problems are not negligible under different bias conditions. The research results tell us that it is necessary to pay much attention to the influence of the initial deviation, the model coefficient deviation and outliers or systematic errors of measurement data on the KF in the LDS.

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“…Compared to these methodologies, the column-wise averaging of epochs allows computing steady-state parameters based on signals with the higher signalto-noise ratio (SNR). Recently, Kalman filtering has emerged as a promising technique for the estimation of 40-Hz ASSR amplitudes 28,29,30 . Implementation of Kalman filtering can improve the detection of steady-state responses closer to the electrophysiological threshold and reduce the time of the hearing test 29 .…”

Section: Introductionmentioning

confidence: 99%

“…Implementation of Kalman filtering can improve the detection of steady-state responses closer to the electrophysiological threshold and reduce the time of the hearing test 29 . Furthermore, stationary responses are not needed to be assumed when a Kalman filtering approach is used to estimate the ASSR amplitude 30 . Nevertheless, only one study has analyzed the time evolution of ASSRs using Kalman filtering 25 .…”

Section: Introductionmentioning

confidence: 99%

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Prado

Otero

Martı́nez-Montes

et al. 2019

JoVE

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Neural entrainment refers to the synchronization of neural activity to the periodicity of sensory stimuli. This synchronization defines the generation of steady-state evoked responses (i.e., oscillations in the electroencephalogram phase-locked to the driving stimuli). The classic interpretation of the amplitude of the steady-state evoked responses assumes a stereotypical time-invariant neural response plus random background fluctuations, such that averaging over repeated presentations of the stimulus recovers the stereotypical response. This approach ignores the dynamics of the steady-state, as in the case of the adaptation elicited by prolonged exposures to the stimulus. To analyze the dynamics of steady-state responses, it can be assumed that the time evolution of the response amplitude is the same in different stimulation runs separated by sufficiently long breaks. Based on this assumption, a method to characterize the time evolution of steady-state responses is presented. A sufficiently large number of recordings are acquired in response to the same experimental condition. Experimental runs (recordings) are column-wise averaged (i.e., runs are averaged but epoch within recordings are not averaged with the preceding segments). The columnwise averaging allows analysis of steady-state responses in recordings with remarkably high signal-to-noise ratios. Therefore, the averaged signal provides an accurate representation of the time evolution of the steady-state response, which can be analyzed in both the time and frequency domains. In this study, a detailed description of the method is provided, using steady-state visually evoked potentials as an example of a response. Advantages and caveats are evaluated based on a comparison with single-trial methods designed to analyze neural entrainment.

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Kalman Filter Based Estimation of Auditory Steady State Response Parameters

Cited by 27 publications

References 24 publications

Electrophysiological assessment of temporal envelope processing in cochlear implant users

Electrophysiological assessment of temporal envelope processing in cochlear implant users

Open Problems in Applications of the Kalman Filtering Algorithm

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

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