Time series analysis in a frequency subband

Broersen, P.M.T.; Waele, S. de

doi:10.1109/tim.2003.814823

Cited by 11 publications

(14 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the decrease in output prediction error was negligible with the increase of filter order, we stopped increasing the filter order. Since we would not have a priori knowledge of process order in a practical situation, a model estimator such as ARMAsel [54] can be used for this purpose. As ARMAsel itself functions based on prediction error evaluations, we might not even need further prediction error evaluations.…”

Section: Resultsmentioning

confidence: 99%

Adaptive multichannel sequential lattice prediction filtering method for ARMA spectrum estimation in subbands

Ozden

2013

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

A multichannel characterization for autoregressive moving average (ARMA) spectrum estimation in subbands is considered in this article. The fullband ARMA spectrum estimation can be realized in two-channels as a special form of this characterization. A complete orthogonalization of input multichannel data is accomplished using a modified form of sequential processing multichannel lattice stages. Matrix operations are avoided, only scalar operations are used, and a multichannel ARMA prediction filter with a highly modular and suitable structure for VLSI implementations is achieved. Lattice reflection coefficients for autoregressive (AR) and moving average (MA) parts are simultaneously computed. These coefficients are then converted to process parameters using a newly developed Levinson-Durbin type multichannel conversion algorithm. Hence, a novel method for spectrum estimation in subbands as well as in fullband is developed. The computational complexity is given in terms of model order parameters, and comparisons with the complexities of nonparametric methods are provided. In addition, the performance is visually and statistically compared against those of the nonparametric methods under both stationary and nonstationary conditions.

show abstract

Section: Resultsmentioning

confidence: 99%

Adaptive multichannel sequential lattice prediction filtering method for ARMA spectrum estimation in subbands

Ozden

2013

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…If we consider a 4-channels MD-SBC, the third subband of s(n), which is evidenced in Fig. 3(a), will correspond to the AFB's output s (2) a (n). This sequence is downsampled by a factor 4, thus its samples are four time less than those of s(n) and its spectrum, shown in Fig.…”

Section: Implementation Of Sbc Filter Banksmentioning

confidence: 99%

“…3(b), will cover the full normalized bandwidth between 0 and π. From a prediction point of view this may not be a good situation, since s (2) a (n) will have high-frequency (normalized) components that are usually difficult to predict because of their rapid changes.…”

Section: Implementation Of Sbc Filter Banksmentioning

confidence: 99%

“…Recent developments in this area have involved different techniques that are typical of signal processing applications. Among them, filter banks are promising tools that can be be used to improve the prediction accuracy [2,7,8]. The basic use of filter banks in this framework consists in the subband decomposition of the sequence to be predicted.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Baseband Filter Banks for Neural Prediction

Panella

Rizzi

2006

2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference On

View full text Add to dashboard Cite

We propose in this paper a new prediction paradigm,\ud which is based on filter banks for subband decomposition\ud of the sequences to be predicted. Filter banks allow the\ud implementation of a parallel computing system, taking the\ud advantage of a faster and more accurate implementation.\ud In particular, we introduce a novel subband decomposition\ud method yielding baseband sequences that are easier to be\ud predicted. The core of the prediction system is based on a\ud neural model, which is trained for each subband using specific\ud embedding techniques. The latter are used in order\ud to optimize the prediction performances when dealing with\ud real-world data sequences, which often possess a chaotic\ud behavior

show abstract

“…One method proposed in [272] uses frequency selective parametric (Auto Regressive Moving Average model) spectral analysis in defined sub-bands.…”

Section: Accuracy Of the Spectral Estimatesmentioning

confidence: 99%

Probing the sub-thalamic nucleus: development of bio-markers from very Local Field Potentials

Varghese¹

View full text Add to dashboard Cite

Deep Brain Stimulation (DBS) surgery for neuro-psychiatric disorders involves the insertion of Micro Electrode Recording (MER) targeting probes into a specific location in the patient's brain to confirm the precise location of the candidate nucleus for the stimulation therapy.The unique geometry and electrical properties of these MERs probes, when inserted into neuron dense tissue such as the Sub-thalamic Nucleus (STN) results in the acquisition of signals which typically contain contributions from the spiking behaviour of multiple nearby neurons in addition to a low frequency component similar to the Local Field Potentials (LFP) generated by more distant neurons (over length scales smaller than typical LFPs). We refer to these signals acquired from the MER probes, which contain both some of the nearby (resolvable) spikes and these smaller scale LFPs as very Local Field Potentials (vLFPs).The unique signal contributions to the vLFPs raise the immediate question what contribution of the signal is best used in order to characterise (in terms of identifying the underlying physiology or detecting changes of) the state of the STN. In this thesis we develop methodologies to analyse vLFPs using both model based and model free analysis of the entire (nearby spiking, distant spiking and non-spiking contributions) vLFP and only the nearby spiking neurons.We apply concepts from Mori-Zwanzig non-equillibrium kinetic theory to develop modelfree estimates of the entire vLFP. With this approach we show that the Non-Markov Parameter (NMP) can be used to identify statistically significant changes in the electrical behaviour of the STN when presented with different neuro-linguistic stimuli. We show that these changes are due to variations in the low frequency bands associated with the power spectrum of the vLFP.We then develop a model-based analysis of the entire vLFP using the renewal theory of stochastic processes. We show that when the ensemble of neural processes forming the vLFP satisfy the assumptions of an independent renewal process model, given a measurement of ii the power spectrum, the common probability distribution driving the spiking statistics of the individual neurons can be identified. We show with simulation that, when the assumptions of the model are satisfied, this approach can outperform state of the art spike sorting algorithms in the challenging situation of identifying the spiking statistics when an unknown number of neurons with near identical spike shapes contribute to the vLFP.Finally we develop spike-only analysis by constructing spike sorting strategies using convex optimisation and clustering theory to identify the precise timing and shapes of the spikes associated with individual neurons nearby to the MER probe. With this approach the spike detection and clustering is performed using a Basis Pursuit De-Noising (BPDN) strategy which is a subset of ℓ 1 regularised least squares techniques. We show that this method outperforms state of the art spike sorting algorithms for a range of signal to n...

show abstract

Time series analysis in a frequency subband

Cited by 11 publications

References 16 publications

Adaptive multichannel sequential lattice prediction filtering method for ARMA spectrum estimation in subbands

Adaptive multichannel sequential lattice prediction filtering method for ARMA spectrum estimation in subbands

Baseband Filter Banks for Neural Prediction

Probing the sub-thalamic nucleus: development of bio-markers from very Local Field Potentials

Contact Info

Product

Resources

About