Analysis and design of periodically time-varying IIR filters, with applications to transmultiplexing

It has long been recognized that the amplitude of the P300 component of event-related brain potentials is sensitive to the degree to which eliciting stimuli are surprising to the observers (Donchin, 1981). While Squires et al. (1976) showed and modeled dependencies of P300 amplitudes from observed stimuli on various time scales, Mars et al. (2008) proposed a computational model keeping track of stimulus probabilities on a long-term time scale. We suggest here a computational model which integrates prior information with short-term, long-term, and alternation-based experiential influences on P300 amplitude fluctuations. To evaluate the new model, we measured trial-by-trial P300 amplitude fluctuations in a simple two-choice response time task, and tested the computational models of trial-by-trial P300 amplitudes using Bayesian model evaluation. The results reveal that the new digital filtering (DIF) model provides a superior account of the trial-by-trial P300 amplitudes when compared to both Squires et al.’s (1976) model, and Mars et al.’s (2008) model. We show that the P300-generating system can be described as two parallel first-order infinite impulse response (IIR) low-pass filters and an additional fourth-order finite impulse response (FIR) high-pass filter. Implications of the acquired data are discussed with regard to the neurobiological distinction between short-term, long-term, and working memory as well as from the point of view of predictive coding models and Bayesian learning theories of cortical function.

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“…Claasen and Mecklenbräuker, 1982; Prater and Loeffler, 1992):

c_{text L MathClass-punc, k} ((), n) MathClass-rel= {falsefalse}_{big\sum}^{ν MathClass-rel= MathClass-bin- \infty} h_{text L MathClass-punc, ν} ((), n) g_{k} ((), ν) MathClass-punc.

…”

Section: Alternation Expectation Of the Squ Modelmentioning

confidence: 99%

A Model-Based Approach to Trial-By-Trial P300 Amplitude Fluctuations

Kolossa¹,

Fingscheidt²,

Wessel³

et al. 2013

Front. Hum. Neurosci.

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“…The output of the time-variant harmonic IIR notch filter is as follows [26]:

v [n] = {140%true \sum}_{m = - \infty}^{n} h [n - m, θ (n)] u [m] = {140%true \sum}_{m = 0}^{\infty} h [m, θ (n)] u [n - m]

where v [ n ], u [ n ], and h [ m , θ ( n )] represent the output, the input, and the time-variant impulse response, respectively, of the time-variant harmonic IIR notch filter with parameter θ ( n ). Equation (3) can be expressed as a z-transform [27]:

V (z) = H (z, θ (n)) U (z)

…”

Section: Methodsmentioning

confidence: 99%

Blockwise PPG Enhancement Based on Time-Variant Zero-Phase Harmonic Notch Filtering

Park

Shin

Lee

2017

Sensors

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So far, many approaches have been developed for motion artifact (MA) reduction from photoplethysmogram (PPG). Specifically, single-input MA reduction methods are useful to apply wearable and mobile healthcare systems because of their low hardware costs and simplicity. However, most of them are insufficiently developed to be used in real-world situations, and they suffer from a phase distortion problem. In this study, we propose a novel single-input MA reduction algorithm based on time-variant forward-backward harmonic notch filtering. To verify the proposed method, we collected real PPG data corrupted by MA and compared it with existing single-input MA reduction methods. In conclusion, the proposed zero-phase line enhancer (ZLE) was found to be superior for MA reduction and exhibited zero phase response.

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“…A LTV system is fully characterized by its Green's function g(n, m) [10], [12, Section 3.5.1], which represents the system response at time n to an impulse at time m. By employing the linear systems superposition principle we can calculate the LTV system output y(n) to an input x(n) as…”

Section: Linear Time-variant Channel Impulse Responsesmentioning

confidence: 99%

“…We name r n (m) the type II LTV CIR. These two structures are inspired on the periodically time-varying ones shown in [10].…”

Section: Linear Time-variant Channel Impulse Responsesmentioning

confidence: 99%

On the mobile‐to‐mobile linear time‐variant shallow‐water acoustic channel response

Sauco-Gallardo

Fernández‐Plazaola

Díez

2017

Int J Communication

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We expose some concepts concerning the channel impulse response (CIR) of linear time-varying (LTV) channels to give a proper characterization of the mobile-to-mobile underwater channel. We find different connections between the linear time-invariant (LTI) CIR of the static channel and two definitions of LTV CIRs of the dynamic mobile-to-mobile channel. These connections are useful to design a dynamic channel simulator from the static channel models available in the literature. Such feature is particularly interesting for overspread channels, which are hard to characterize by a measuring campaign. Specifically, the shallow water acoustic (SWA) channel is potentially overspread due the signal low velocity of propagation which prompt long delay spread responses and great Doppler effect. Furthermore, from these connections between the LTI static CIRs and the LTV dynamic CIRS, we find that the SWA mobile-to-mobile CIR does not only depend on the relative velocity between transceivers, but also on the absolute velocity of each of them referred to the velocity of * This work is under review for Journal publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

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Analysis and design of periodically time-varying IIR filters, with applications to transmultiplexing

Cited by 43 publications

References 13 publications

A Model-Based Approach to Trial-By-Trial P300 Amplitude Fluctuations

A Model-Based Approach to Trial-By-Trial P300 Amplitude Fluctuations

Blockwise PPG Enhancement Based on Time-Variant Zero-Phase Harmonic Notch Filtering

On the mobile‐to‐mobile linear time‐variant shallow‐water acoustic channel response

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