Digital spectral analysis
01 Feb 1983-IEEE Transactions on Acoustics, Speech, and Signal Processing (IEEE)-Vol. 31, Iss: 1, pp 241-242
About: This article is published in IEEE Transactions on Acoustics, Speech, and Signal Processing.The article was published on 1983-02-01. It has received 144 citations till now.
Citations
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TL;DR: In this paper, the current status of lattice-dynamical calculations in crystals, using density-functional perturbation theory, with emphasis on the plane-wave pseudopotential method, is reviewed.
Abstract: This article reviews the current status of lattice-dynamical calculations in crystals, using density-functional perturbation theory, with emphasis on the plane-wave pseudopotential method. Several specialized topics are treated, including the implementation for metals, the calculation of the response to macroscopic electric fields and their relevance to long-wavelength vibrations in polar materials, the response to strain deformations, and higher-order responses. The success of this methodology is demonstrated with a number of applications existing in the literature.
6,917 citations
TL;DR: The results as a whole demonstrate the importance of proper spatial filter selection for maximizing the signal-to-noise ratio and thereby improving the speed and accuracy of EEG-based communication.
895 citations
Cites methods from "Digital spectral analysis"
...Every 100 ms, the most recent 200-ms segment from each channel was analyzed by an autoregressive algorithm (Marple , 1987), and the square root of power in a 4 or 5-Hz wide frequency band centered at 10, 12 or 20 Hz was calculated (Subject A, 18-22 Hz; B, 7.5-12.5 Hz; C, 17.5-22.5 Hz; D, 10-14 Hz)....
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...Every 100 ms, the most recent 200-ms segment from each channel was analyzed by an autoregressive algorithm (Marple, 1987), and the square root of power in a 4 or 5-Hz wide...
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...The waveforms resulting from each of these methods were then subjected to an autoregressive spectral analysis (maximum entropy method (MEM) (Marple, 1987))....
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...The waveforms resulting from each of these methods were then subjected to an autoregressive spectral analysis (maximum entropy method (MEM) (Marple, 1987) )....
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TL;DR: A computer program for advanced heart rate variability analysis that calculates all the commonly used time- and frequency-domain measures of HRV as well as the nonlinear Poincaré plot and parametric and nonparametric spectrum estimates are calculated.
751 citations
Cites background from "Digital spectral analysis"
...The prefix NN stands for normal-to-normal intervals (i.e. intervals between consecutive QRS complexes resulting from sinus node depolarizations)....
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TL;DR: Performance Assessment and prediction tools are introduced for continuous assessment and prediction of a particular product's performance, ultimately enable proactive maintenance to prevent machine from breakdowns.
577 citations
Cites background from "Digital spectral analysis"
...Finally, Section 5 summarizes conclusions of this work and outlines guidelines for possible future work....
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TL;DR: The effect of different events on the EEG signal, and different signal processing methods used to extract the hidden information from the signal are discussed in detail.
Abstract: The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They are highly random in nature and may contain useful information about the brain state. However, it is very difficult to get useful information from these signals directly in the time domain just by observing them. They are basically non-linear and nonstationary in nature. Hence, important features can be extracted for the diagnosis of different diseases using advanced signal processing techniques. In this paper the effect of different events on the EEG signal, and different signal processing methods used to extract the hidden information from the signal are discussed in detail. Linear, Frequency domain, time - frequency and non-linear techniques like correlation dimension (CD), largest Lyapunov exponent (LLE), Hurst exponent (H), different entropies, fractal dimension(FD), Higher Order Spectra (HOS), phase space plots and recurrence plots are discussed in detail using a typical normal EEG signal.
449 citations
Cites methods from "Digital spectral analysis"
...Burg method Burg method is based on minimizing forward and backward prediction errors while satisfying the LevinsonDurbin recursion [67, 56, 82]....
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References
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TL;DR: In this paper, the current status of lattice-dynamical calculations in crystals, using density-functional perturbation theory, with emphasis on the plane-wave pseudopotential method, is reviewed.
Abstract: This article reviews the current status of lattice-dynamical calculations in crystals, using density-functional perturbation theory, with emphasis on the plane-wave pseudopotential method. Several specialized topics are treated, including the implementation for metals, the calculation of the response to macroscopic electric fields and their relevance to long-wavelength vibrations in polar materials, the response to strain deformations, and higher-order responses. The success of this methodology is demonstrated with a number of applications existing in the literature.
6,917 citations
TL;DR: A computer program for advanced heart rate variability analysis that calculates all the commonly used time- and frequency-domain measures of HRV as well as the nonlinear Poincaré plot and parametric and nonparametric spectrum estimates are calculated.
751 citations
TL;DR: Performance Assessment and prediction tools are introduced for continuous assessment and prediction of a particular product's performance, ultimately enable proactive maintenance to prevent machine from breakdowns.
577 citations
TL;DR: The effect of different events on the EEG signal, and different signal processing methods used to extract the hidden information from the signal are discussed in detail.
Abstract: The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They are highly random in nature and may contain useful information about the brain state. However, it is very difficult to get useful information from these signals directly in the time domain just by observing them. They are basically non-linear and nonstationary in nature. Hence, important features can be extracted for the diagnosis of different diseases using advanced signal processing techniques. In this paper the effect of different events on the EEG signal, and different signal processing methods used to extract the hidden information from the signal are discussed in detail. Linear, Frequency domain, time - frequency and non-linear techniques like correlation dimension (CD), largest Lyapunov exponent (LLE), Hurst exponent (H), different entropies, fractal dimension(FD), Higher Order Spectra (HOS), phase space plots and recurrence plots are discussed in detail using a typical normal EEG signal.
449 citations
TL;DR: This paper illustrates the use of the recently introduced method of partial directed coherence in approaching how interactions among neural structures change over short time spans that characterize well defined behavioral states.
336 citations