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PubMed2004

Spectral characterization of ongoing and auditory event-related brain processes.

Brain processes phase-locked to stimuli can be readily observed with electro- and magnetoencephalography (EEG & MEG, respectively) using stimulus-triggered averaging of the measured signal. The detection of non-phase-locked brain processes depends on the method used for analyzing the unaveraged data. Here we introduce a technique, partition-referenced moment (PRM) power spectrum, which uses established spectral estimation algorithms but yields a power spectrum with sharp, easily distinguishable peaks in an otherwise level spectrum even when the signal (such as EEG & MEG) is of the one-over-frequency-slope type. Employing this method and wavelet transforms, we show that transient auditory brain responses are followed by dispersed small-magnitude power reductions. Power reductions occurred around 400-600 ms and were specific to ongoing 10 Hz-oscillations. The PRM-method also indicated ongoing oscillations in the 15-30 Hz frequency range where power reductions occurred at around 200-400 ms. Thus, the presented methods enable the straightforward detection of ongoing brain oscillations and their association with event-related power changes.

Acoustic Stimulation↗
PubMed2006

[Methods applied to muscle fatigue assessment using surface myoelectric signals].

Surface myoelectric signal analysis has proved effective for assessing the electrical manifestations of localized muscle fatigue. In the past, the analysis of muscle fatigue was restricted to isometric, constant force contractions due to the limitation of signal processing technique. The development and recent availability of spectral estimation techniques specifically designed for nonstationary signal analysis have made it possible to extend the employment of muscle fatigue assessment to dynamic contractions, thus opening new application fields such as ergonomics rehabilitation and sports medicine. This paper reviews the current study achievements of using surface myoelectric signals in muscle fatigue assessment, particularly in that during dynamic contractions. The conclusions provide theoretical bases for encouraging further studies on the mechanisms of muscle fatigue.

Electromyography↗
PubMed1991

[Analysis of the interference electromyogram of human soleus muscle after exposure to vibration].

The properties of m. soleus surface EMG recorded under conditions of voluntary contraction against vibrational stimulation were studied using vibration-triggered averaging and spectral estimates. The averaging procedure indicated EMG bursts locked to the vibration cycle. Narrow peaks appeared in the EMG spectrum at vibration frequency and harmonics. These effects were more pronounced in rectified EMG at low vibration frequencies (30-70 Hz) and in EMG at high frequencies (70-120 Hz). The disappearance of the peak after ischemic blockade preceded that of the tendon reflex. The peak normalized to the EMG power decreased when the force was enlarged. The peak augmented with prolonged contraction under vibration. The results are suggested to reflect alterations of the relative weight of the excitatory inflow through short spindle-motoneuron connections in the overall motoneuron inflow.

Adult↗
PubMed1990

Forty hertz (40Hz) and sensorimotor rhythms (SMR) as EEG state variables and related evoked potentials.

There is evidence that certain brain rhythms may signal the occurrence of different brain states. Support for this hypothesis may be provided by evidence that processing of information is changed during different brain states. The prestimulus period proceeding the auditory evoked potential (AEP) is converted to a power spectral estimate. These power spectra are used to sort for the AEP when certain power values for the (36-42Hz) and the (11-16Hz) are reached in the prestimulus period of the sensory motor cortex, nucleus accumbers and amygdala. Attempts are made to measure the performance of the 40Hz and SMR estimators. These studies are made in the cerebral cortical and subcortical regions in cats. In these studies D and L isomers of amphetamine are used to induce a chemical brain state changes. The AEP are selected in the amphetamine altered states based on the prestimulus auto spectra.

Analog-Digital Conversion↗
PubMed1987

Assessment of analgesia by evoked cerebral potential measurements in humans.

This paper gives a brief account of new methods for the evaluation of pain and analgesia in healthy male volunteers by use of evoked cerebral potentials. In pain research, the so-called late components with latencies between 100 and 400 ms are usually used, but these potentials are non-specific, depending on a variety of factors e.g. on the activity of the ongoing electroencephalogram (EEG) on the subject's arousal/attention mechanisms, on the novelty of the stimulus and on its painfulness. Therefore, if evoked cerebral potentials in response to phasic pain stimuli are to be evaluated quantitatively, constant experimental conditions are essential, including subject selection to obtain as uniform a sample of volunteers as possible. Latency variation in evoked potential components in single studies makes signal averaging methods rather inaccurate to predict the effects of weak analgesics upon cerebral potentials in relation to time. Therefore single trial studies have been performed using transformation of post-stimulus EEG activity in terms of frequency. Because of the short duration of the evoked potentials, various parametric spectral estimators have been investigated for their advantages in EEG analysis. Frequency transformation of stimulus-induced cerebral activity by means of the maximum entropy method gives an enormous increase in the power in the 2-4 Hz frequency band with a very constantly located maximum. Examples are given showing that in this way it might be possible to monitor the time course of efficacy of even so-called weak analgesics.

Brain↗
PubMed1985

Analysis of biomedical signals by means of linear modeling.

The recording and subsequent analysis of electrical signals of physiological origin constitutes an important aspect of current biomedical research. A versatile method for the analysis of such signals is based on linear, i.e., autoregressive (moving average) modeling. These techniques are based on fitting a hypothetical model to the signal under observation. These models are capable of generating the original signal by a linear combination of past observations and past and present noise samples. High resolution spectral estimates can be obtained in this way. Also, the often small number of model coefficients offer a concise description of the signal and may be used for classification purposes. Other applications entail the detection of nonstationarities, data-compression, and signal enhancement. In this review, linear modeling methods for the analysis of electroencephalograms, electro- and phono-cardiograms, electromyograms, and gastrointestinal signals are surveyed.

Digestive System Physiological Phenomena↗
PubMed1994

Improved white noise method in the evaluation of linear hearing-aids characteristics.

In this paper an alternative method of determining the standardized electroacoustical parameters for hearing aids is discussed. This is done by using white noise as the input stimulus and a high resolution algorithm, Burg's method, to obtain the spectral estimation of the frequency response. Three types of hearing-aid are analyzed according to both the pure tonal and white noise method and their standardized computed electroacoustical characteristics are compared. The results show deep differences at the low frequency range and a close match at high frequencies (HFA-ref. test and SSPL-90 dBSPL).

Acoustic Stimulation↗
PubMed1996

Estimation of the power spectral density in nonstationary cardiovascular time series: assessing the role of the time-frequency representations (TFR).

Spectral analysis of cardiovascular series has been proposed as a noninvasive tool for investigating the autonomic control of the cardiovascular system. The analysis of such series during autonomic tests requires high resolution estimators that are capable to track the transients of the tests. A comparative evaluation has been made among classical (FFT based), autoregressive (both block and sequential mode) and time-frequency representation (TFR) based power spectral estimators. The evaluation has been performed on artificial data that have typical patterns of the nonstationary series. The results documented the superiority of the TFR approach when a sharp time resolution is required. Moreover, the test on a RR-like series has shown that the smoothing operation is effective for rejecting TFR cross-terms when a simple, two-three components series is concerned. Finally, the preliminary application of the selected methods to real RR interval time series obtained during some autonomic tests has shown that the TFR are capable to correctly represent the transient of the series in the joint time-frequency domain.

Algorithms↗
PubMed1996

Nonstationarity broadening reduction in pulsed Doppler spectrum measurements using time-frequency estimators.

The spectral width of Doppler signals is used as measure of lesion-induced flow disturbance. Its estimation accuracy is compromised using the conventional short-term Fourier transform (STFT) since this method implicitly assumes signal stationarity during the signal window while the Doppler signals from arteries are markedly nonstationary. The Wigner-Ville (WVD), Choi-Williams (CWD) and Bessel distributions (BD), specifically designed for nonstationary signals, have been optimized for spectral width estimation accuracy and compared to the STFT under different signal to noise ratios using simulated Doppler signals of known time-frequency characteristics. The optimum parameter values for each method were determined as a Hanning window duration of 10 ms for the STFT, 40 ms for the WVD and CWD and 20 ms for the BD and dimensionless time-frequency smoothing constant values of five in the CWD and two in the BD. Thresholding was used to reduce the effect of cross terms and side lobes in the WVD and BD. With no added noise the WVD gave the lowest estimation error followed by the CWD. At signal-to-noise ratios (SNR's) of 10 dB and 20 dB the CWD and BD had similar errors and were markedly better than the other estimators. Overall the CWD gave the best performance.

Fourier Analysis↗
PubMed2000

Estimation of surface electromyogram spectral alteration using reduced-order autoregressive model.

A new method is proposed, based on the pole phase angle (PPA) of a second-order autoregressive (AR) model, to track spectral alteration during localised muscle fatigue when analysing surface myo-electric (ME) signals. Both stationary and non-stationary, simulated and real ME signals are used to investigate different methods to track spectral changes. The real ME signals are obtained from three muscles (the right vastus lateralis, rectus femoris and vastus medialis) of six healthy male volunteers, and the simulated signals are generated by passing Gaussian white-noise sequences through digital filters with spectral properties that mimic the real ME signals. The PPA method is compared, not only with spectra-based methods, such as Fourier and AR, but also with zero crossings (ZCs) and the first AR coefficient that have been proposed in the literature as computer efficient methods. By comparing the deviation (dev), in percent, between the linear regression of the theoretical and estimated mean frequencies of the power spectra for simulated stationary (s) and non-stationary (ns) signals, in general, it is found that the PPA method (devs = 4.29; devns = 1.94) gives a superior performance to ZCs (dvs = 8.25) and the first AR coefficient (4.18 < devs < 21.8; 0.98 < devns < 4.36) but performs slightly worse than spectra-based methods (0.33 < devs < 0.79; 0.41 < devns < 1.07). However, the PPA method has the advantage that it estimates spectral alteration without calculating the spectra and therefore allows very efficient computation.

Adult↗
PubMed2005

Noninvasive estimation of tissue temperature via high-resolution spectral analysis techniques.

We address the noninvasive temperature estimation from pulse-echo radio frequency signals from standard diagnostic ultrasound imaging equipment. In particular, we investigate the use of a high-resolution spectral estimation method for tracking frequency shifts at two or more harmonic frequencies associated with temperature change. The new approach, employing generalized second-order statistics, is shown to produce superior frequency shift estimates when compared to conventional high-resolution spectral estimation methods Seip and Ebbini (1995). Furthermore, temperature estimates from the new algorithm are compared with results from the more commonly used echo shift method described in Simon et al. (1998).

Algorithms↗
PubMed1991

Comparison of different estimators of electromyographic spectral shifts during work when applied on short test contractions.

EMG was recorded during short test contractions performed during occupational work. Mean power frequency (MPF) and median frequency (MF), calculated from EMG spectra, and zero crossing rate (ZC) of the EMG signal were compared as estimators of local muscular fatigue. The results show that there is a systematic difference between the three estimates. The difference can be explained by dividing the effect of muscle fatigue on EMG spectra into a motor unit action potential velocity effect and a firing statistics effect. Furthermore, it is shown that the phenomenon of increasing estimators of EMG spectrum shift is unlikely to be caused by motor unit action potential velocity increase. Successive recruitment of new motor units is suggested as a feasible explanation.

Electromyography↗
PubMed2002

Use of Spectral Radiance to Estimate In-Season Biomass and Grain Yield in Nitrogen- and Water-Stressed Corn.

Current technologies for measuring plant water status are limited, while recently remote sensing techniques for estimating N status have increased with limited research on the interaction between the two stresses. Because plant water status methods are time-consuming and require numerous observations to characterize a field, managers could benefit from remote sensing techniques to assist in irrigation and N management decisions. A 2-yr experiment was initiated to determine specific wavelengths and/or combinations of wavelengths indicative of water stress and N deficiencies, and to evaluate these wavelengths for estimating in-season biomass and corn (Zea mays L.) grain yield. The experiment was a split-plot design with three replications. The treatment structure had five N rates (0, 45, 90, 134, and 269 kg N ha(-1)) and three water treatments [dryland, 0.5 evapotranspiration (ET), and full ET]. Canopy spectral radiance measurements (350-2500 nm) were taken at various growth stages (V6-V7, V13-V16, and V14-R1). Specific wavelengths for estimating crop biomass, N concentration, grain yield, and chlorophyll meter readings changed with growth stage and sampling date. Changes in total N and biomass in the presence of a water stress were estimated using near-infrared (NIR) reflectance and the water absorption bands. Reflectance in the green and NIR regions were used to estimate total N and biomass without water stress. Reflectance at 510, 705, and 1135 nm were found for estimating chlorophyll meter readings regardless of year or sampling date.

Journal Article↗
PubMed1992

Abstract processes in texture discrimination.

In this study some experiments on texture segmentation are reported using the local Gabor power spectrum. The techniques applied are: (1) supervised pixel classification; (2) boundary detection by spectral dissimilarity estimation; (3) region-based segmentation based on Gaussian spectral estimation; and (4) the same as (3) but based on central moments of the local spectrum. It is shown that very-acceptable-to-excellent results can be obtained. It is argued, however, that the shortcomings of region-based and boundary-based approaches require that both processes should act in parallel, not only in digital image processing but also in the modelling of visual perception.

Form Perception↗
PubMed1984

[A new computational method for estimating X-ray spectral distributions].

A new computational method is described for estimating the exposure-rate spectral distributions of X-rays from attenuation data measured with various filtrations. The estimation problem of X-ray spectra is formulated as the numerical computation of solving a set of linear equation with an ill-conditional nature. In this paper, the singular-value decomposition technique, which differs from the iterative method, is applied to this singular numerical computation problem. The principle of the analysis method is based on that the response matrix of filtrations can be decomposed into some inherent component matrices. X-ray spectral distributions are then represented in a simple combination of some component curves, so that the estimation process can be systematically constructed. The singularity in its computation is removed by selecting the components of the combination, and a performance index is also presented for the optimal selection. The feasibility of the proposed method is studied in detail in a computer simulation using a hypothetical X-ray spectrum produced by assuming experimental conditions. The application results are also shown about the spectral distribution from a 140 kV constant voltage X-ray source.

Computers↗
PubMed2000

A new statistical approach to detecting significant activation in functional MRI.

There are many ways to detect activation patterns in a time series of observations at a single voxel in a functional magnetic resonance imaging study. The critical problem is to estimate the statistical significance, which depends on the estimation of both the magnitude of the response to the stimulus and the serial dependence of the time series and especially on the assumptions made in that estimation. We show that for experimental designs with periodic stimuli, only a few aspects of the serial dependence are important and these can be estimated reliably via nonparametric estimation of the spectral density of the time series, whereas existing techniques are biased by their assumptions. The linear model with (stationary) serially dependent errors can be analyzed entirely in frequency domain, and doing so provides many insights. In particular, we introduce a technique to detect periodic activations and show that it has a distribution theory that enables us to assign significance levels down to 1 in 100,000, levels which are needed when a whole brain image is under consideration. Nonparametric spectral density estimation is shown to be self-calibrating and accurate when compared to several other time-domain approaches. The technique is especially resistant to high frequency artefacts that we have found in some datasets and we demonstrate that time-domain approaches may be sufficiently susceptible to these effects to give misleading results. The method is easily generalized to handle event-related designs. We found it necessary to consider the trends in the time series carefully and use nonlinear filters to remove the trends and robust techniques to remove "spikes." Using this in connection with our techniques allows us to detect activations in clumps of a few (even one) voxel in periodic designs, yet produce essentially no false positive detections at any voxels in null datasets.

Acoustic Stimulation↗