Improving spatial and temporal resolution in evoked EEG responses using surface Laplacians

Spline generated surface Laplacian temporal wave forms are presented as a method to improve both spatial and temporal resolution of evoked EEG responses. Middle latency and the N1 components of the auditory evoked response were used to compare potential-based methods with surface Laplacian methods in the time domain. Results indicate that surface Laplacians provide better estimates of underlying cortical activity than do potential wave forms. Spatial discrimination among electrode sites was markedly better with surface Laplacian than with potential wave forms. Differences in the number and latencies of peaks, and their topographic distributions, were observed for surface Laplacian, particularly during the time period encompassing the middle latency responses. Focal activities were observed in surface Laplacian wave forms and topographic maps which were in agreement with previous findings from auditory evoked response studies. Methodological issues surrounding the application of spline methods to the time domain are also discussed. Surface Laplacian methods in the time domain appear to provide an improved way for studying evoked EEG responses by increasing temporal and spatial resolution of component characteristics.     

Location of innervation zone determined with multichannel surface electromyography using an optical flow technique.

Multichannel surface electromyography has developed towards more channels and higher spatial resolution. This allows the study of multichannel electromyograms as images of the potential distribution on the skin. In this paper, a method that estimates the motion of the potential distribution using an optical-flow-based technique is introduced. The optical flow is a vector field that describes how images change with time. The aim of this study was to introduce a new method for innervation zone (IZ) localization and to evaluate its performance. The new method was compared with a method that uses the position of the lowest root-mean-square (RMS) value in an electrode array as an estimate of the IZ localization. Comparisons were made with both simulated signals and with recorded multichannel electromyogram signals. Simulations showed that the methods performed similarly for high signal-to-noise ratio (SNR) and that the optical-flow-based method was superior for lower SNR. When the experimental signals were used, localization with the optical-flow-based method gave a mean absolute deviation of 2.4mm from the location given by an expert group. The lowest RMS method gave a significantly higher deviation (13.6mm). Due to the low computational complexity of the optical flow algorithm it is possible to get the estimations of the IZ localization in real time.     

Removal of ECG interference from surface respiratory electromyography

The signal to noise ratio (SNR) of surface respiratory electromyography signal is very low. Indeed EMG signal is contaminated by different types of noise especially the cardiac artefact ECG. This article explores the problem of removing ECG artefact from respiratory EMG signal. The new method uses an adaptive structure with an electrocardyographic ECG reference signal carried out by wavelet decomposition. The proposed algorithm requires only one channel to both estimating the adaptive filter input reference noise and the respiratory EMG signal. This new technique demonstrates how two steps will be combined: the first step decomposes the signal with forward discrete wavelet transform into sub-bands to get the wavelet coefficients. Then, an improved soft thresholding function was applied. And the ECG input reference signal is reconstructed with the transformed coefficients whereas, the second uses an adaptive filter especially the LMS one to remove the ECG signal. After trying statistical as well as mathematical analysis, the complete investigation ensures that all details and steps make proof that our rigorous method is appropriate. Compared to the results obtained using previous techniques, the results achieved using the new algorithm show a significant improvement in the efficiency of the ECG rejection.     

Measuring surface potential components necessary for transmembrane current computation using microfabricated arrays

This study was designed to test the feasibility of using microfabricated electrodes to record surface potentials with sufficiently fine spatial resolution to measure the potential gradients necessary for improved computation of transmembrane current density. To assess that feasibility, we recorded unipolar electrograms from perfused rabbit right ventricular free wall epicardium (n = 6) using electrode arrays that included 25-microm sensors fabricated onto a flexible substrate with 75-microm interelectrode spacing. Electrode spacing was therefore on the size scale of an individual myocyte. Signal conditioning adjacent to the sensors to control lead noise was achieved by routing traces from the electrodes to the back side of the substrate where buffer amplifiers were located. For comparison, recordings were also made using arrays built from chloridized silver wire electrodes of either 50-microm (fine wire) or 250-microm (coarse wire) diameters. Electrode separations were necessarily wider than with microfabricated arrays. Comparable signal-to-noise ratios (SNRs) of 21.2 +/- 2.2, 32.5 +/- 4.1, and 22.9 +/- 0.7 for electrograms recorded using microfabricated sensors (n = 78), fine wires (n = 78), and coarse wires (n = 78), respectively, were found. High SNRs were maintained in bipolar electrograms assembled using spatial combinations of the unipolar electrograms necessary for the potential gradient measurements and in second-difference electrograms assembled using spatial combinations of the bipolar electrograms necessary for surface Laplacian (SL) measurements. Simulations incorporating a bidomain representation of tissue structure and a two-dimensional network of guinea pig myocytes prescribed following the Luo and Rudy dynamic membrane equations were completed using 12.5-microm spatial resolution to assess contributions of electrode spacing to the potential gradient and SL measurements. In those simulations, increases in electrode separation from 12.5 to 75.0, 237.5, and 875.0 microm, which were separations comparable to the finest available with our microfabricated, fine wire, and coarse wire arrays, led to 10%, 42%, and 81% reductions in maximum potential gradients and 33%, 76%, and 96% reductions in peak-to-peak SLs. Maintenance of comparable SNRs for source electrograms was therefore important because microfabrication provides a highly attractive methods to achieve spatial resolutions necessary for improved computation of transmembrane current density.     

Modeling nonlinear errors in surface electromyography due to baseline noise: A new methodology

The surface electromyographic (EMG) signal is often contaminated by some degree of baseline noise. It is customary for scientists to subtract baseline noise from the measured EMG signal prior to further analyses based on the assumption that baseline noise adds linearly to the observed EMG signal. The stochastic nature of both the baseline and EMG signal, however, may invalidate this assumption. Alternately, “true” EMG signals may be either minimally or nonlinearly affected by baseline noise. This information is particularly relevant at low contraction intensities when signal-to-noise ratios (SNR) may be lowest. Thus, the purpose of this simulation study was to investigate the influence of varying levels of baseline noise (approximately 2–40% maximum EMG amplitude) on mean EMG burst amplitude and to assess the best means to account for signal noise. The simulations indicated baseline noise had minimal effects on mean EMG activity for maximum contractions, but increased nonlinearly with increasing noise levels and decreasing signal amplitudes. Thus, the simple baseline noise subtraction resulted in substantial error when estimating mean activity during low intensity EMG bursts. Conversely, correcting EMG signal as a nonlinear function of both baseline and measured signal amplitude provided highly accurate estimates of EMG amplitude. This novel nonlinear error modeling approach has potential implications for EMG signal processing, particularly when assessing co-activation of antagonist muscles or small amplitude contractions where the SNR can be low.     

Few-sweep estimation of evoked potential based on a generalized subspace approach

A generalized subspace approach is proposed for single channel brain evoked potential (EP) extraction from background electroencephalogram (EEG) signal. The method realizes the optimum estimate of EP signal from the observable noisy signal. The underlying principle is to project the signal and noise into signal and noise coefficient subspace respectively by applying projection matrix at first. Secondly, coefficient weighting matrix is achieved based on the autocorrelation matrices of the noise and the noisy signal. With the coefficient weighting matrix, we can remove the noise projection coefficients and estimate the signal ones. EP signal is then obtained by averaging the signals estimated with the reconstruction matrix. Given different signal-to-noise ratio (SNR) conditions, the algorithm can estimate the EP signal with only two sweeps observable noisy signals. Our approach is shown to have excellent capability of estimating EP signal even in poor SNR conditions. The interference of spontaneous EEG has been eliminated with significantly improved SNR. The simulation results have demonstrated the effectiveness and superior performance of the proposed method.     

Electro/magnetoencephalography beamforming with spatial restrictions based on sparsity

We present a source localization method for electroencephalographic (EEG) and magnetoencephalographic (MEG) data which is based on an estimate of the sparsity obtained through the eigencanceler (EIG), which is a spatial filter whose weights are constrained to lie in the noise subspace. The EIG provides rejection of directional interferences while minimizing noise contributions and maintaining specified beam pattern constraints. In our case, the EIG is used to estimate the sparsity of the signal as a function of the position, then we use this information to spatially restrict the neural sources to locations out of the sparsity maxima. As proof of the concept, we incorporate this restriction in the “classical” linearly constrained minimum variance (LCMV) source localization approach in order to enhance its performance. We present numerical examples to evaluate the proposed method using realistically simulated EEG/MEG data for different signal-to-noise (SNR) conditions and various levels of correlation between sources, as well as real EEG/MEG measurements of median nerve stimulation. Our results show that the proposed method has the potential of reducing the bias on the search of neural sources in the classical approach, as well as making it more effective in localizing correlated sources.     

视网膜单细胞成像技术研究

建立了一台基于37单元变形镜、Shack-hartman波前像差传感器和12位科研级CCD相机的自适应光学视网膜相机。采用一个中心波长为679nm的超辐射二极管(SLD)作为相机的光源,通过将超辐射二极管和多模光纤耦合,显著减小了SLD光源的空间相干性,从而消除了散斑噪声对成像的影响。多模光纤的输出提供了一种高亮度、均匀照明的光源,使人眼视网膜单细胞成像的速度达到4．8幅／秒。     

Influence of Noise Correction on Intra- and Inter-Subject Variability of Quantitative Metrics in Diffusion Kurtosis Imaging

Diffusion kurtosis imaging (DKI) is a promising extension of diffusion tensor imaging, giving new insights into the white matter microstructure and providing new biomarkers. Given the rapidly increasing number of studies, DKI has a potential to establish itself as a valuable tool in brain diagnostics. However, to become a routine procedure, DKI still needs to be improved in terms of robustness, reliability, and reproducibility. As it requires acquisitions at higher diffusion weightings, results are more affected by noise than in diffusion tensor imaging. The lack of standard procedures for post-processing, especially for noise correction, might become a significant obstacle for the use of DKI in clinical routine limiting its application. We considered two noise correction schemes accounting for the noise properties of multichannel phased-array coils, in order to improve the data quality at signal-to-noise ratio (SNR) typical for DKI. The SNR dependence of estimated DKI metrics such as mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) is investigated for these noise correction approaches in Monte Carlo simulations and in in vivo human studies. The intra-subject reproducibility is investigated in a single subject study by varying the SNR level and SNR spatial distribution. Then the impact of the noise correction on inter-subject variability is evaluated in a homogeneous sample of 25 healthy volunteers. Results show a strong impact of noise correction on the MK estimate, while the estimation of FA and MD was affected to a lesser extent. Both intra- and inter-subject SNR-related variability of the MK estimate is considerably reduced after correction for the noise bias, providing more accurate and reproducible measures. In this work, we have proposed a straightforward method that improves accuracy of DKI metrics. This should contribute to standardization of DKI applications in clinical studies making valuable inferences in group analysis and longitudinal studies.     

Functional and morphological cardiac magnetic resonance imaging of mice using a cryogenic quadrature radiofrequency coil

Cardiac morphology and function assessment by magnetic resonance imaging is of increasing interest for a variety of mouse models in pre-clinical cardiac research, such as myocardial infarction models or myocardial injury/remodeling in genetically or pharmacologically induced hypertension. Signal-to-noise ratio (SNR) constraints, however, limit image quality and blood myocardium delineation, which crucially depend on high spatial resolution. Significant gains in SNR with a cryogenically cooled RF probe have been shown for mouse brain MRI, yet the potential of applying cryogenic RF coils for cardiac MR (CMR) in mice is, as of yet, untapped. This study examines the feasibility and potential benefits of CMR in mice employing a 400 MHz cryogenic RF surface coil, compared with a conventional mouse heart coil array operating at room temperature. The cryogenic RF coil affords SNR gains of 3.0 to 5.0 versus the conventional approach and hence enables an enhanced spatial resolution. This markedly improved image quality - by better deliniation of myocardial borders and enhanced depiction of papillary muscles and trabeculae - and facilitated a more accurate cardiac chamber quantification, due to reduced intraobserver variability. In summary the use of a cryogenically cooled RF probe represents a valuable means of enhancing the capabilities of CMR of mice.     

Using two-dimensional spatial information in decomposition of surface EMG signals.

Recently, high-density surface EMG electrode grids and multi-channel amplifiers became available for non-invasive recording of human motor units (MUs). We present a way to decompose surface EMG signals into MU firing patterns, whereby we concentrate on the importance of two-dimensional spatial differences between the MU action potentials (MUAPs). Our method is exemplified with high-density EMG data from the vastus lateralis muscle of a single subject. Bipolar and Laplacian spatial filtering was applied to the monopolar raw signals. From the single recording in this subject six different simultaneously active MUs could be distinguished using the spatial differences between MUAPs in the direction perpendicular to the muscle fiber direction. After spike-triggered averaging, 125-channel two-dimensional MUAP templates were obtained. Template-matching allowed tracking of all MU firings. The impact of spatial information was measured by using subsets of the MUAP templates, either in parallel or perpendicular to the muscle fiber direction. The use of one-dimensional spatial information perpendicular to the muscle fiber direction was superior to the use of a linear array electrode in the longitudinal direction. However, to detect the firing events of the MUs with a high accuracy, as needed for instance for estimation of firing synchrony, two-dimensional information from the complete grid electrode appears essential.     

线性整合放电模型的相干共振

研究了神经递质以随机点序列释放和电压门控离子通道噪声共同作用下,线性整合放电模型的相干共振现象。基于分形布朗运动和改进的欧拉方法,得到了神经元膜电压分布和神经元放电峰峰间隔的信噪比。结果表明,神经元放电的峰峰间隔是神经递质的达到强度、离子通道噪声强度的非单调函数。适当的神经递质到达强度和离子通道噪声强度使峰峰间隔的信噪比出现最大值,即出现相干共振现象。     

Sensitive detection of unlabeled oligonucleotides using a paired surface plasma waves biosensor

Detection of unlabeled oligonucleotides using surface plasmon resonance (SPR) is difficult because of the oligonucleotides' relatively lower molecular weight compared with proteins. In this paper, we describe a method for detecting unlabeled oligonucleotides at low concentration using a paired surface plasma waves biosensor (PSPWB). The biosensor uses a sensor chip with an immobilized probe to detect a target oligonucleotide via sequence-specific hybridization. PSPWB measures the demodulated amplitude of the heterodyne signal in real time. In the meantime, the ratio of the amplitudes between the detected output signal and reference can reduce the excess noise from the laser intensity fluctuation. Also, the common-path propagation of p and s waves cancels the common phase noise induced by temperature variation. Thus, a high signal-to-noise ratio (SNR) of the heterodyne signal is detected. The sequence specificity of oligonucleotide hybridization ensures that the platform is precisely discriminating between target and non-target oligonucleotides. Under optimized experimental conditions, the detected heterodyne signal increases linearly with the logarithm of the concentration of target oligonucleotide over the range 0.5-500 pM. The detection limit is 0.5 pM in this experiment. In addition, the non-target oligonucleotide at concentrations of 10 pM and 10nM generated signals only slightly higher than background, indicating the high selectivity and specificity of this method. Different length of perfectly matched oligonucleotide targets at 10-mer, 15-mer and 20-mer were identified at the concentration of 150 pM.     

Spatial distribution of surface immunoglobulin on B lymphocytes : Local ordering

Maps of the spatial distribution of surface immunoglobulin on B lymphocytes obtained by freezeetch electron microscopy were analysed mathematically. The pictures were automatically scanned by a digital microdensitometer and the number and coordinates of the immunoglobulin molecules determined. A statistical measure, the radial distribution function, g(r), commonly used to study the structure of liquids, was computed for each map. The radial distribution function of surface immunoglobulin was found to closely resemble g(r) for fluids, indicating the presence of shortrange (but not long-range) order. It was determined that a given surface immunoglobulin molecule has a high probability of being surrounded by other immunoglobulins at distances approximately one-half the mean interparticle spacing. From this one can conclude that the distribution of surface immunoglobulin is non-random and is characterized by local clustering. The mean number of first nearest neighbors surrounding each surface immunoglobulin, computed from g(r), was found to be near two. This value is consistent with a degree of linear order in the topographic distribution of immunoglobulin. The radial distribution function provides a method of quantitating the amount of clustering in a spatial distribution. This function may prove useful in accessing the amount of receptor cross-linking necessary to trigger cellular responses, and in elucidating mechanisms for aggregation and movement of membrane macromolecules.     

Towards toxicity detection using a lab-on-chip based on the integration of MOEMS and whole-cell sensors

A lab-on-chip consisting of a unique integration of whole-cell sensors, a MOEMS (Micro-Opto-Electro-Mechanical-System) modulator, and solid-state photo-detectors was implemented for the first time. Whole-cell sensors were genetically engineered to express a bioluminescent reporter (lux) as a function of the lac promoter. The MOEMS modulator was designed to overcome the inherent low frequency noise of solid-state photo-detectors by means of a previously reported modulation technique, named IHOS (Integrated Heterodyne Optical System). The bio-reporter signals were modulated prior to photo-detection, increasing the SNR of solid-state photo-detectors at least by three orders of magnitude. Experiments were performed using isopropyl-beta-d-thiogalactopyranoside (IPTG) as a preliminary step towards testing environmental toxicity. The inducer was used to trigger the expression response of the whole-cell sensors testing the sensitivity of the lab-on-chip. Low intensity bio-reporter optical signals were measured after the whole-cell sensors were exposed to IPTG concentrations of 0.1, 0.05, and 0.02 mM. The experimental results reveal the potential of this technology for future implementation as an inexpensive massive method for rapid environmental toxicity detection.     

SPHARA - A Generalized Spatial Fourier Analysis for Multi-Sensor Systems with Non-Uniformly Arranged Sensors: Application to EEG

Important requirements for the analysis of multichannel EEG data are efficient techniques for signal enhancement, signal decomposition, feature extraction, and dimensionality reduction. We propose a new approach for spatial harmonic analysis (SPHARA) that extends the classical spatial Fourier analysis to EEG sensors positioned non-uniformly on the surface of the head. The proposed method is based on the eigenanalysis of the discrete Laplace-Beltrami operator defined on a triangular mesh. We present several ways to discretize the continuous Laplace-Beltrami operator and compare the properties of the resulting basis functions computed using these discretization methods. We apply SPHARA to somatosensory evoked potential data from eleven volunteers and demonstrate the ability of the method for spatial data decomposition, dimensionality reduction and noise suppression. When employing SPHARA for dimensionality reduction, a significantly more compact representation can be achieved using the FEM approach, compared to the other discretization methods. Using FEM, to recover 95% and 99% of the total energy of the EEG data, on average only 35% and 58% of the coefficients are necessary. The capability of SPHARA for noise suppression is shown using artificial data. We conclude that SPHARA can be used for spatial harmonic analysis of multi-sensor data at arbitrary positions and can be utilized in a variety of other applications.     

Improving the SNR of EEG generated by deep sources with weighted multielectrode leads

We have developed a multielectrode lead technique to improve the signal-to-noise ratio (SNR) of scalp-recorded electroencephalography (EEG) signals generated deep in the brain. The basis of the method lies in optimization of the measurement sensitivity distribution of the multielectrode lead. We claim that two factors improve the SNR in a multielectrode lead: (1) the sensitivity distribution of a multielectrode lead is more specific in measuring signals generated deep in the brain and (2) spatial averaging of noise occurs when several electrodes are applied in the synthesis of a multielectrode lead. We showed theoretically that within a three-layer spherical head model the sensitivity distributions of multielectrode leads are more specific for deep sources than those of traditional bipolar leads. We also estimated with simulations and with preliminary measurements the total improvement in SNR achieved by both the more specific lead field and spatial averaging. Results obtained with simulations and with experimental measurements show an apparent improvement in SNR obtained with multielectrode leads. This encourages for future development of the method.     

Noise and signal power and their effects on evoked potential estimation

Signal power, noise power and their ratio (SNR) are important variables underlying estimation of evoked potential signals, yet, they are rarely explicitly considered in the design or analysis of EP experiments. A model is developed which relates the reliability of the average evoked potential (AEP) wave form to signal power, noise power, SNR, and the number of single trials included in the average. Measurements taken from auditory and visual EP experimental in elderly subjects show that noise power is highly reliable across experimental conditions and probably reflects global CNS anatomic or physiologic factors. In contrast, signal power and SNR are variable across conditions and sensory modalities, but are stable across replications. Thus signal power reflects CNS processes specific to the experimental paradigm. These results have importance for EP estimation. The expected reliability of the AEP cannot be adequately predicted from estimates of a subject's noise power, or from SNR estimated under different experimental conditions. These findings suggest the need for on-line estimation of SNR during data acquisition to ensure adequate reliability of AEPs.     

Effects of Physiological Internal Noise on Model Predictions of Concurrent Vowel Identification for Normal-Hearing Listeners

Previous studies have shown that concurrent vowel identification improves with increasing temporal onset asynchrony of the vowels, even if the vowels have the same fundamental frequency. The current study investigated the possible underlying neural processing involved in concurrent vowel perception. The individual vowel stimuli from a previously published study were used as inputs for a phenomenological auditory-nerve (AN) model. Spectrotemporal representations of simulated neural excitation patterns were constructed (i.e., neurograms) and then matched quantitatively with the neurograms of the single vowels using the Neurogram Similarity Index Measure (NSIM). A novel computational decision model was used to predict concurrent vowel identification. To facilitate optimum matches between the model predictions and the behavioral human data, internal noise was added at either neurogram generation or neurogram matching using the NSIM procedure. The best fit to the behavioral data was achieved with a signal-to-noise ratio (SNR) of 8 dB for internal noise added at the neurogram but with a much smaller amount of internal noise (SNR of 60 dB) for internal noise added at the level of the NSIM computations. The results suggest that accurate modeling of concurrent vowel data from listeners with normal hearing may partly depend on internal noise and where internal noise is hypothesized to occur during the concurrent vowel identification process.     

NMR structure of stem-loop D from human rhinovirus-14

The 5'-cloverleaf of the picornavirus RNA genome is essential for the assembly of a ribonucleoprotein replication complex. Stem-loop D (SLD) of the cloverleaf is the recognition site for the multifunctional viral protein 3Cpro. This protein is the principal viral protease, and its interaction with SLD also helps to position the viral RNA-dependent RNA polymerase (3Dpol) for replication. Human rhinovirus-14 (HRV-14) is distinct from the majority of picornaviruses in that its SLD forms a cUAUg triloop instead of the more common uYACGg tetraloop. This difference appears to be functionally significant, as 3Cpro from tetraloop-containing viruses cannot bind the HRV-14 SLD. We have determined the solution structure of the HRV-14 SLD using NMR spectroscopy. The structure is predominantly an A-form helix, but with a central pyrimidine-pyrimidine base-paired region and a significantly widened major groove. The stabilizing hydrogen bonding present in the uYACGg tetraloop was not found in the cUAUg triloop. However, the triloop uses different structural elements to present a largely similar surface: sequence and underlying architecture are not conserved, but key aspects of the surface structure are. Important structural differences do exist, though, and may account for the observed cross-isotype binding specificities between 3Cpro and SLD.