Principal component elimination method for the improvement of S/Nin evoked neuromagnetic field measurements

In the study of magnetoencephalography, it is important to obtain evoked fields with good signal-to-noise ratios (S/N) and with a small number of epochs in averaging. The noises are considered to be mainly spontaneous neuromagnetic fields. In the present study, we propose a method to improve the S/N. The basic principle of this method is the elimination of a principal component (PC) of multichannel-recorded neuromagnetic fields, utilizing the synchronized characteristics of spontaneous rhythmic activities dominating the fields. The proposed method is, therefore, called the principal component elimination method (PCEM). PCEM was applied to neuromagnetic fields measured by a 37-channel superconducting quantum interference device system, on which computer-generated evoked fields were superposed, in order to examine possible improvement in S/N. It was found that elimination of the first PC could improve the S/N of the evoked fields. The improvement in S/N with elimination of the first PC, compared to conventional simple averaging, increased with increases in the number of epochs and reached more than 50% after averaging over 128 epochs. PCEM also reduced the number of epochs needed in averaging to about half of that needed in conventional simple averaging.     

Detection of Stimulus-Related (Evoked Response) Activity in the Electroencephalogram (EEG)

In clinical neurophysiology the visual evaluation of averaged evoked waveforms may be rather difficult, allowing no definite answer to the question of whether stimulus-related activity is present or not. This problem calls for appropriate statistical procedures testing for the presence of significant response activity. In the present work the commonly used averaging technique was supplemented by phase value measurements of Fourier components in the poststimulus sample functions. If the latter contain no evoked activity, the phase values may be assumed to be uniformly distributed in the interval 0°-360°(the null hypothesis assumption). Contrary to this, an aggregation of phase values and, hence, a nonuniform distribution will result if evoked activity is present. The statistical problem may thus be reduced to testing a null-hypothesis assumption of uniformity. In the present investigation a statistical procedure of the Kolmogorov-Smirnov type, Kuiper's VN test, appeared to be a valuable aid in the detection of stimulusrelated EEG activity.     

Enhancement of phase clustering in the EEG/MEG gamma frequency band anticipates transitions to paroxysmal epileptiform activity in epileptic patients with known visual sensitivity

A new analytical method for quantifying brain activity from magnetoelectroencephalogram (MEG) and electroencephalogram (EEG) recordings during periodic light stimulation is proposed. It consists in estimating the phase clustering of harmonically related frequency components of a subject's MEG/EEG responses evoked by the light stimulation. The method was developed to test the hypothesis that changes in the dynamics of brain systems in the course of intermittent photic stimulation (IPS) may precede the transition to seizure activity in photosensitive patients. We assumed that such changes would be reflected in the phase of harmonic components of the evoked responses. Thus, we determined the phase clustering for different harmonic components of these MEG/EEG signals. We found that the patients who develop epileptiform discharges during IPS present an enhancement of the phase clustering index at the gamma frequency band, compared with that at the driving frequency. We introduce a quantity--relative phase clustering index (rPCI)--by means of which this enhancement can be quantified. We argue that this quantity reflects the degree of excitability of the underlying dynamical system and it can indicate presence of nonlinear dynamics. rPCI can be applied to detect transitions to epileptic seizure activity in patients with known sensitivity to IPS.     

Demodulation of phase-modulated light using birefringent crystals

The application of birefringent crystals to accomplish the demodulation of phase-modulated light is considered. A particular combination of crystals and a polarization insensitive photodetector is termed a birefringent demodulator, and shown to have the properties that it will 1) convert a phase-modulated light signal into an amplitude-modulated light signal and subsequently detect it in a nearly lossless manner, and 2) simultaneously suppress or balance out an incident amplitude-modulated light signal. A time domain version of the "Jones Calculus" is formulated and used to analyze the demodulator. Conversion efficiency, suppression efficiency, and bandwidth are considered. Experimental results demonstrating PM to AM conversion and AM suppression at microwave modulation frequencies between 2 and 12 Gc are given.     

Single-Trial Evoked Brain Responses Modeled by Multivariate Matching Pursuit

We present a new approach to the analysis of brain evoked electromagnetic potentials and fields. Multivariate version of the matching pursuit algorithm (MMP) performs an iterative, exhaustive search for waveforms, which optimally fit to signal structures, persistent in all the responses (trials) with the same time of occurrence, frequency, phase, and time width, but varying amplitude. The search is performed in a highly redundant time--frequency dictionary of Gabor functions, i.e., sines modulated by Gaussians. We present the feasibility of such a single-trial MMP analysis of the auditory M100 response, using an illustrative dataset acquired in a magnetoencephalographic (MEG) measurement with auditory stimulation with sinusoidal 1-kHz tones. We find that the morphology of the M100 estimate obtained from simple averaging of single trials can be very well explained by the average reconstruction with a few Gabor functions that parametrize those single trials. The M100 peak amplitude of single-trial reconstructions is observed to decrease with repetitions, which indicates habituation to the stimulus. This finding suggests that certain waveforms fitted by MMP could possibly be related to physiologically distinct components of evoked magnetic fields, which would allow tracing their dynamics on a single-trial level.      

Linear minimum mean-square error filtering for evoked responses: application to fetal MEG

This paper describes a linear minimum mean-squared error (LMMSE) approach for designing spatial filters that improve the signal-to-noise ratio (SNR) of multiepoch evoked response data. This approach does not rely on availability of a forward solution and thus is applicable to problems in which a forward solution is not readily available, such as fetal magnetoencephalography (fMEG). The LMMSE criterion leads to a spatial filter that is a function of the autocorrelation matrix of the data and the autocorrelation matrix of the signal. The signal statistics are unknown, so we approximate the signal autocorrelation matrix using the average of the data across epochs. This approximation is reasonable provided the mean of the noise is zero across epochs and the signal mean is significant. An analysis of the error incurred using this approximation is presented. Calculations of SNR for the exact and approximate LMMSE filters and simple averaging for the rank-1 signal case are shown. The effectiveness of the method is demonstrated with simulated evoked response data and fetal MEG data.     

Visual evoked potential actuated brain computer interface: a brain-actuated cursor system

Flash visual evoked potential (FVEP), induced by OFF-to-ON flash, i.e. flash onset, in a light emitting diode (LED) was used to control four cursor movements (left, right, up, down), and left- and right-button clicks on a screen menu. ON or OFF duration in each flashing sequence was designed to be random so that all flashing sequences were mutually independent. Since FVEPs are time-locked and phase-locked to flash onsets of gazed LEDs, segmenting EEG signals based on the flash onsets of each flashing sequence followed by averaging will sharpen epochs evoked by gazed LEDs. Four inexperienced subjects were asked to generate a sequence of cursor commands. Mean recognition and transfer rates were 88% and 3.74 s/command, respectively.     

Maximum contrast beamformer for electromagnetic mapping of brain activity

Beamforming technique can be applied to map the neuronal activities from magnetoencephalographic/electroencephalographic (MEG/EEG) recordings. One of the major difficulties of the scalar-type MEG/EEG beamformer is the determination of accurate dipole orientation, which is essential to an effective spatial filter. This paper presents a new beamforming technique which exploits a maximum contrast criterion to maximize the ratio of the neuronal activity estimated in a specified active state to the activity estimated in a control state. This criterion leads to a closed-form solution of the dipole orientation. Experiments with simulation, phantom, and finger-lifting data clearly demonstrate the effectiveness, efficiency, and accuracy of the proposed method.     

Measurements of Signal Components in Single Visualy Evoked Brain Potentials

A two step procedure is described for measuring the characteristics of visual evoked brain potentials. First, the recorded waveforms are processed by a filter designed to minimize the mean square error produced by the ongoing EEG. This filter is different for each subject and is based on certain statistical properties of the measured data. Second, the filtered potentials are searched automatically by a computer to determine the existence and location of the individual components in the responses. By aligning the corresponding components in different waveforms and averaging over the waveform segment in the immediate vicinity of the peak, a latency corrected average is obtained that provides a new representation of the response waveform.     

Electromagnetic brain mapping

There has been tremendous advances in our ability to produce images of human brain function. Applications of functional brain imaging extend from improving our understanding of the basic mechanisms of cognitive processes to better characterization of pathologies that impair normal function. Magnetoencephalography (MEG) and electroencephalography (EEG) (MEG/EEG) localize neural electrical activity using noninvasive measurements of external electromagnetic signals. Among the available functional imaging techniques, MEG and EEG uniquely have temporal resolutions below 100 ms. This temporal precision allows us to explore the timing of basic neural processes at the level of cell assemblies. MEG/EEG source localization draws on a wide range of signal processing techniques including digital filtering, three-dimensional image analysis, array signal processing, image modeling and reconstruction, and, blind source separation and phase synchrony estimation. We describe the underlying models currently used in MEG/EEG source estimation and describe the various signal processing steps required to compute these sources. In particular we describe methods for computing the forward fields for known source distributions and parametric and imaging-based approaches to the inverse problem     

Combined MEG and EEG source imaging by minimization of mutual information

Though very frequently assumed, the necessity to operate a joint processing of simultaneous magnetoencephalography (MEG) and electroencephalography (EEG) recordings for functional brain imaging has never been clearly demonstrated. However, the very last generation of MEG instruments allows the simultaneous recording of brain magnetic fields and electrical potentials on the scalp. But the general fear regarding the fusion between MEG and EEG data is that the drawbacks from one modality will systematically spoil the performances of the other one without any consequent improvement. This is the case for instance for the estimation of deeper or radial sources with MEG. In this paper, we propose a method for a cooperative processing of MEG and EEG in a distributed source model. First, the evaluation of the respective performances of each modality for the estimation of every dipole in the source pattern is made using a conditional entropy criterion. Then, the algorithm operates a preprocessing of the MEG and EEG gain matrices which minimizes the mutual information between these two transfer functions, by a selective weighting of the MEG and EEG lead fields. This new combined EEG/MEG modality brings major improvements to the localization of active sources, together with reduced sensitivity to perturbations on data.     

In vivo measurement of the brain and skull resistivities using an EIT-based method and the combined analysis of SEF/SEP data

Results of "in vivo" measurements of the skull and brain resistivities are presented for six subjects. Results are obtained using two different methods, based on spherical head models. The first method uses the principles of electrical impedance tomography (EIT) to estimate the equivalent electrical resistivities of brain (rhobrain), skull (rhoskull) and skin (rhoskin) according to. The second one estimates the same parameters through a combined analysis of the evoked somatosensory cortical response, recorded simultaneously using magnetoencephalography (MEG) and electroencephalography (EEG). The EIT results, obtained with the same relative skull thickness (0.05) for all subjects, show a wide variation of the ratio rhoskull/rhobrain among subjects (average = 72, SD = 48%). However, the rhoskull/rhobrain ratios of the individual subjects are well reproduced by combined analysis of somatosensory evoked fields (SEF) and somatosensory evoked potentials (SEP). These preliminary results suggest that the rhoskull/rhobrain variations over subjects cannot be disregarded in the EEG inverse problem (IP) when a spherical model is used. The agreement between EIT and SEF/SEP points to the fact that whatever the source of variability, the proposed EIT-based method 相似文献   

Coherence-weighted wiener filtering of somatosensory evokedpotentials

In this paper, we present a Wiener filtering (WF) approach for extraction of somatosensory evoked potentials (SEPs) from the background electroencephalogram (EEG), with sweep-to-sweep variations in its signal power. To account for the EEG power variations, WF is modified by iteratively weighting the power spectrum using the coherence function. Coherence-weighted Wiener filtering (CWWF) is able to extract SEP waveforms, which have a greater level of detail as compared with conventional time-domain averaging (TDA). Using CWWF, the components of the SEP show significantly less variability. As such, CWWF should be useful as an important diagnostic tool able to detect minimal changes in the SEP. In an experimental study of cerebral hypoxia, CWWF is shown to be more responsive to detection of injury than WF or TDA.     

Multi-source Neural Activity Estimation and Sensor Scheduling: Algorithms and Hardware Implementation

Electroencephalography (EEG) and magnetoencephalography (MEG) measurements are used to localize neural activity by solving the electromagnetic inverse problem. In this paper, we propose a new approach based on the particle filter implementation of the probability hypothesis density filter (PF-PHDF) to automatically estimate the unknown number of time-varying neural dipole sources and their parameters using EEG/MEG measurements. We also propose an efficient sensor scheduling algorithm to adaptively configure EEG/MEG sensors at each time step to reduce total power consumption. We demonstrate the improved performance of the proposed algorithms using simulated neural activity data. We map the algorithms onto a Xilinx Virtex-5 field-programmable gate array (FPGA) platform and show that it only takes 10 ms to process 100 data samples using 6,400 particles. Thus, the proposed system can support real-time processing of an EEG/MEG neural activity system with a sampling rate of up to 10 kHz.     

Three-Dimensional Imaging of Complex Neural Activation in Humans From EEG

Electro- or magnetoencephalography (EEG/MEG) are of utmost advantage in studying transient neuronal activity and its timing with respect to behavior in the working human brain. Direct localization of the neural substrates underlying EEG/MEG is commonly achieved by modeling neuronal activity as dipoles. However, the success of neural source localization with the dipole model has only been demonstrated in relatively simple localization tasks owing to the simplified model and its insufficiency in differentiating cortical sources with different extents. It would be of great interest to image complex neural activation with multiple sources of different cortical extensions directly from EEG/MEG. We have investigated this crucial issue by adding additional parameters to the dipole model, leading to the multipole model to better represent the extended sources confined to the convoluted cortical surface. The localization of multiple cortical sources is achieved by using the subspace source localization method with the multipole model. Its performance is evaluated with simulated data as compared with the dipole model, and further illustrated with the real data obtained during visual stimulations in human subjects. The interpretation of the localization results is fully supported by our knowledge about their anatomic locations and functional magnetic resonance imaging data in the same experimental setting. Methods for estimating multiple neuronal sources at cortical areas will facilitate our ability to characterize the cortical electrical activity from simple, early sensory components to more complex networks, such as in visual, motor, and cognitive tasks.     

Signals and Noise in Evoked Brain Potentials

Event-related brain potentials measured with scalp electrodes are always corrupted by unrelated electrical discharges occurring in the brain. These unrelated electrical discharges, generally referred to as noise, have temporal and spectral characteristics similar to evoked potential waveforms, and they greatly increase the difficulty of detecting and estimating the parameters of the evoked potential waveforms themselves. This problem has been analyzed by computing the probability distributions for measured amplitudes and latencies of ERP components measured in the presence of the ongoing EEG. The analytical results have been verified over a wide range of signal-to-noise ratios by computer simulation. Comparisons of theoretical results to measured data indicate that the latency variations found experimentally greatly exceed what would be expected if they were due only to additive noise. It may be concluded, therefore, that the single ERP is not a signal whose components are deterministically related to the stimulus, but is made up of components that shift significantly in both amplitude and latency from one stimulus application to the next. Using the expressions developed in the paper, it is possible to separate the contributions to the variance due to interference from the ongoing EEG and that inherent in the ERP.     

Estimation of neural dynamics from MEG/EEG cortical current density maps: application to the reconstruction of large-scale cortical synchrony

There is a growing interest in elucidating the role of specific patterns of neural dynamics--such as transient synchronization between distant cell assemblies--in brain functions. Magnetoencephalography (MEG)/electroencephalography (EEG) recordings consist in the spatial integration of the activity from large and multiple remotely located populations of neurons. Massive diffusive effects and poor signal-to-noise ratio (SNR) preclude the proper estimation of indices related to cortical dynamics from nonaveraged MEG/EEG surface recordings. Source localization from MEG/EEG surface recordings with its excellent time resolution could contribute to a better understanding of the working brain. We propose a robust and original approach to the MEG/EEG distributed inverse problem to better estimate neural dynamics of cortical sources. For this, the surrogate data method is introduced in the MEG/EEG inverse problem framework. We apply this approach on nonaveraged data with poor SNR using the minimum norm estimator and find source localization results weakly sensitive to noise. Surrogates allow the reduction of the source space in order to reconstruct MEG/EEG data with reduced biases in both source localization and time-series dynamics. Monte Carlo simulations and results obtained from real MEG data indicate it is possible to estimate non invasively an important part of cortical source locations and dynamic and, therefore, to reveal brain functional networks.     

Assessing the Relevance of fMRI-Based Prior in the EEG Inverse Problem: A Bayesian Model Comparison Approach

Characterizing the cortical activity from electro- and magneto-encephalography (EEG/MEG) data requires solving an ill-posed inverse problem that does not admit a unique solution. As a consequence, the use of functional neuroimaging, for instance, functional Magnetic Resonance Imaging (fMRI), constitutes an appealing way of constraining the solution. However, the match between bioelectric and metabolic activities is desirable but not assured. Therefore, the introduction of spatial priors derived from other functional modalities in the EEG/MEG inverse problem should be considered with caution. In this paper, we propose a Bayesian characterization of the relevance of fMRI-derived prior information regarding the EEG/MEG data. This is done by quantifying the adequacy of this prior to the data, compared with that obtained using an noninformative prior instead. This quantitative comparison, using the so-called Bayes factor, allows us to decide whether the informative prior should (or not) be included in the inverse solution. We validate our approach using extensive simulations, where fMRI-derived priors are built as perturbed versions of the simulated EEG sources. Moreover, we show how this inference framework can be generalized to optimize the way we should incorporate the informative prior.     

The Influence of Model Parameters on BEG/MEG Single Dipole Source Estimation

This paper deals with source localization and strength estimation based on EEG and MEG data. It describes an estimation method (inverse procedure) which uses a four-spheres model of the head and a single current dipole. The dependency of the inverse solution on model parameters is investigated. It is found that sphere radii and conductivities influence especially the strength of the EEG equivalent dipole and not its location or direction. The influence on the equivalent dipole of the gradiometer is investigated. In general the MEG produces better location estimates than the EEG whereas the reverse is found for the component estimates. An inverse solution simultaneously based on EEG and MEG data appears slightly better than the average of separate EEG and MEG solutions. Variances of parameter estimators which can be calculated on the basis of a linear approximation of the model, were tested by Monte Carlo simulations.