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Background and Objectives: Magnetoencephalography (MEG) could be a valuable tool in the presurgical evaluation of drug-resistant epilepsy (DRE), especially when the initial evaluation is inconclusive. In this retrospective study, we describe the profile of MEG in patients with DRE and normal magnetic resonance imaging (MRI). Methods: We included patients with focal epilepsy and normal MRI who underwent presurgical evaluation for DRE. MEG profiles of these patients, including the frequency of spikes, density of clusters, number of clusters, and concordance with video electroencephalography (VEEG), were analyzed. Results: Of the 73 patients included, magnetic source imaging (MSI) provided localizing information in 51 (69.9%) patients. Among patients with localizing MEG findings, localizing information on VEEG too was noted in 42 (57.5% of the whole cohort). Thirty-one (42.5%) patients had concordant findings with region-specific localization, six (8.2%) patients had partial concordance, and five (6.8%) subjects showed discordant findings. There was a moderate agreement for the presumed epileptogenic zone in comparing findings derived from MEG and VEEG (kappa value of 0.451, P < 0.001). The agreement was lower when MEG localized to the frontal lobe (kappa value of 0.379, P = 0.001) than the temporal lobe (kappa value 0.442, P = 0.002). Conclusions: MEG can provide localizing information in most patients with a normal MRI. A moderate degree of agreement between localization by MEG and VEEG was noted. These findings highlight the usefulness of MSI in the presurgical evaluation of MRI-negative DRE. Keywords: Magnetic source imaging, MRI-negative epilepsy, presurgical evaluation of epilepsy, Author(s): Ajay Asranna [1]; Asheeb Abdulhak [1]; Lakshminarayanapuram Gopal Viswanathan [1]; Ravindranandh Chowdary Mundlamuri [1]; Raghavendra Kenchaiah [1]; Mariyappa Narayanan [2]; Bhargava Gautham [2]; Velumurugan Jayabal [2]; Rose Dawn Bharath [...]

Background: Dysfunction in the default mode network (DMN), a group of cortical areas more active during the resting state, has been linked to attentional deficits and symptoms associated with attention-deficit/hyperactivity disorder (ADHD). Prior imaging studies have shown decreased functional connectivity between DMN nodes in patients with ADHD, primarily between anterior and posterior regions. Using magnetoencephalography (MEG), we evaluated phase coherence (i.e., functional connectivity) among regions of the DMN in healthy controls and adults with ADHD before and after stimulant therapy. Methods: We obtained a resting-state MEG recording for all participants. Magnetoencephalography data were transformed into a ~30 node regional source model using inverse spatial filtering, including regions corresponding to the DMN. We computed the zero-lag phase coherence between these regions pairwise for 5 distinct frequency bands, and we assessed group and medication effects. Results: Twelve adults with and 13 without ADHD participated in our study. Functional connectivity was stronger between particular node pairs and showed frequency--specific effects. Unmedicated patients showed reduced phase locking between posterior cingulate/precuneus regions (PCC) and right inferior parietal cortices (RIPL), and between medial prefrontal regions (MPFC) and the left inferior parietal region (LIPL) and the PCC. Unmedicated patients had increased phase locking between the RIPL and LIPL regions compared with controls. Administration of stimulants improved phase locking abnormalities along the MPFC-PCC and LIPL-RIPL pathways in patients with ADHD. Limitations: Modest sample size and lack of duration of patient treatment history may limit the generalizability of our findings. Conclusion: Adults with ADHD exhibit hyper- and hypoconnectivity between regions of the DMN during rest, which were suppressed after stimulant medication administration., Introduction Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurobehavioural disorders, affecting 8% of children and persisting in 4% of adults in the United States. (1) Cardinal symptoms of [...]

We compared judgements of the simultaneity or asynchrony of visual stimuli in individuals with autism spectrum disorders (ASD) and typically-developing controls using Magnetoencephalography (MEG). Two vertical bars were presented simultaneously or non-simultaneously with two different stimulus onset delays. Participants with ASD distinguished significantly better between real simultaneity (0 ms delay between two stimuli) and apparent simultaneity (17 ms delay between two stimuli) than controls. In line with the increased sensitivity, event-related MEG activity showed increased differential responses for simultaneity versus apparent simultaneity. The strongest evoked potentials, observed over occipital cortices at about 130 ms, were correlated with performance differences in the ASD group only. Superior access to early visual brain processes in ASD might underlie increased resolution of visual events in perception. Keywords High-functioning autism * Asperger syndrome * Event timing * Visual simultaneity * Magnetoencephalography, Introduction There is increasing interest in the perceptual and sensory peculiarities associated with autism spectrum disorders (ASD), in part because although these symptoms are not currently included in the diagnostic [...]

We studied visual perception using an annular random-dot motion stimulus called the racetrack. We recorded neural activity using magnetoencephalography while subjects viewed variants of this stimulus that contained no inherent motion or various degrees of embedded motion. Subjects reported seeing rotary motion during viewing of all stimuli. We found that, in the absence of any motion signals, patterns of brain activity differed between states of motion perception and nonperception. Furthermore, when subjects perceived motion, activity states within the brain did not differ across stimuli of different amounts of embedded motion. In contrast, we found that during periods of nonperception brain-activity states varied with the amount of motion signal embedded in the stimulus. Taken together, these results suggest that during perception the brain may lock into a stable state in which lower-level signals are suppressed. racetrack illusion | visual motion perception doi/ 10.1073/pnas.1009857108

A spatiotemporal framework for estimating trial-to-trial variability in evoked response (ER) data is presented. Spatial and temporal bases capture the aspects of the response that are consistent across trials, while the basis expansion coefficients represent the variable components of the response. We focus on the simplest case of constant spatiotemporal response shape and varying amplitude across trials. Two different constraints on the amplitude evolution are employed to effectively integrate the individual responses and improve robustness at low SNR. The linear dynamical system response constraint estimates the current trial amplitude as an unknown constant scaling of the estimate in the previous trial plus zero-mean Gaussian noise with unknown variance. The independent response constraint estimates response amplitudes across trials as independent Gaussian random variables having unknown mean and variance. We develop a generalized expectation-maximization algorithm to obtain the maximum-likelihood (ML) estimates of the signal waveform, noise covariance matrix, and unknown constraint parameters. ML source localization is achieved by scanning the likelihood over different sets of spatial bases. We demonstrate the variability estimation and source localization effectiveness of the proposed algorithms using both real and simulated ER data. Index Terms--Event-related magnetoencephalography (MEG)/ electroencephalography (EEG), expectation-maximization (EM), independent response (IR), linear dynamical system response (LDSR), maximum likelihood (ML), trial-to-trial variability.

We study the effect of the head shape variations on the EEG/magnetoencephalography (MEG) forward and inverse problems. We build a random head model such that each sample represents the head shape of a different individual and solve the forward problem assuming this random head model, using a polynomial chaos expansion. The random solution of the forward problem is then used to quantify the effect of the geometry when the inverse problem is solved with a standard head model. The results derived with this approach are valid for a continuous family of head models, rather than just for a set of cases. The random model consists of three random surfaces that define layers of different electric conductivity, and we built an example based on a set of 30 deterministic models from adults. Our results show that for a dipolar source model, the effect of the head shape variations on the EEG/MEG inverse problem due to the random head model is slightly larger than the effect of the electronic noise present in the sensors. The variations in the EEG inverse problem solutions are due to the variations in the shape of the volume conductor, while the variations in the MEG inverse problem solutions, larger than the EEG ones, are caused mainly by the variations of the absolute position of the sources in a coordinate system based on anatomical landmarks, in which the magnetometers have a fixed position. Index Terms--EEG/magnetoencephalography (MEG) average head model, polynomial chaos expansion (PCE), sparse grids, stochastic modeling.

In this paper, we present a simple method to find networks of time-correlated brain sources, using a singular value decomposition (SVD) analysis of the source matrix estimated after any linear distributed inverse problem in magnetoencephalography (MEG) and electroencephalography (EEG). Despite the high dimension of the source space, our method allows for the rapid computation of the source matrix. In order to do this, we use the linear relationship between sensors and sources, and show that the SVD can be calculated through a simple and fast computation. We show that this method allows the estimation of one or several global networks of correlated sources without calculating a coupling coefficient between all pairs of sources. A series of simulations studies were performed to estimate the efficiency of the method. In order to illustrate the validity of this approach in experimental conditions, we used real MEG data from a visual stimulation task on one test subject and estimated, in different time windows of interest, functional networks of correlated sources. Index Terms--Correlation, electroencephalography (EEG), functional networks, magnetoencephalography (MEG), multivariate analysis, singular value decomposition (SVD).

Visual awareness has been proposed to depend on recurrent processing in early visual cortex areas including the primary visual cortex (V1). Here, we address this hypothesis with high spatiotemporal resolution magnetoencephalographic recordings in subjects performing a substitution masking paradigm. Neural activity reflecting awareness is assessed by directly comparing the neuromagnetic response elicited by effectively and ineffectively masked targets after the proportion of trials leading to masking was individually adjusted to match the proportion of trials without masking. This revealed a modulation of recurrent activity in the primary visual cortex rapidly after the onset of the feed-forward sweep of processing in striate and extrastriate areas but significantly before the onset of attention-dependent recurrent modulations in V1. Our data provide direct support for the notion that (i) recurrent processing in V1 correlates with visual awareness and (ii) that attention and awareness involve distinct recurrent processing operations. magnetoencephalography | visual attention | visual awareness

Repeated exposure to a stimulus facilitates its processing. This is reflected in faster and more accurate identification, reduced perceptual identification thresholds, and more efficient classifications for repeated compared with novel items. Here, we test a hypothesis that this experience-based behavioral facilitation is a result of enhanced communication between distinct cortical regions, which reduces local processing demands. A magnetoencephalographic investigation revealed that repeated object classification led to decreased neural responses in the prefrontal cortex and temporal cortex. Critically, this decrease in absolute activity was accompanied by greater neural synchrony (a measure of functional connectivity) between these regions with repetition. Additionally, the onset of the enhanced interregional synchrony predicted the degree of behavioral facilitation. These findings suggest that object repetition results in enhanced interactions between brain regions, which facilitates performance and reduces processing demands on the regions involved. functional communication | learning | magnetoencepholography | synchrony | memory

The reconstruction of neuronal current sources from magneto- and/or electroencephalography (MEG/EEG) measurements is referred to as an inverse problem. A precursor to most inverse algorithms is a forward transfer, or lead-field, matrix, in which the rows correspond to MEG and/or EEG measurement sites, and each column captures the linear response to a particular unit source. Simple models of the head, such as concentric spheres, result in analytic expressions for the lead-field. More realistic head models, such as those based on medical imagery, require numeric simulations. A straightforward, though inefficient, way to obtain the lead-field is to perform one forward simulation for each source, resulting in one column of the lead-field. For MEG/EEG inverse problems, however, the potential sources (rows) far outnumber the measurement sites (columns). Two approaches have been described for computing the EEG lead-field with a number of forward simulations equal to the number of measurement rows, rather than the number of source columns. One of these approaches is based on the principle of electric reciprocity, and the other approach is based on linear-algebraic manipulations of the forward problem. For the MEG lead-field, only a linear-algebraic approach has been described for numeric approaches such as the finite element method. This paper describes a reciprocal approach for the MEG lead-field and discusses implementation details for both approaches. Index Terms--Finite element methods, inverse problems, lead-field basis, magnetoencephalography, reciprocity.

The aim of this study was to assess whether independent component analysis (ICA) could be valuable to remove power line noise, cardiac, and ocular artifacts from magnetoencephalogram (MEG) background activity. The MEGs were recorded from 11 subjects with a 148-channel whole-head magnetometer. We used a statistical criterion to estimate the number of independent components. Then, a robust ICA algorithm decomposed the MEG epochs and several methods were applied to detect those artifacts. The whole process had been previously tested on synthetic data. We found that the line noise components could be easily detected by their frequency spectrum. In addition, the ocular artifacts could be identified by their frequency characteristics and scalp topography. Moreover, the cardiac artifact was better recognized by its skewness value than by its kurtosis one. Finally, the MEG signals were compared before and after artifact rejection to evaluate our method. Index Terms--Artifact rejection, higher order statistics, independent component analysis (ICA), magnetoencephalography (MEG).

Electrophysiological signals of the developing fetal brain and heart can be investigated by fetal magnetoencephalography (fMEG). During such investigations, the fetal heart activity and that of the mother should be monitored continuously to provide an important indication of current well-being. Due to physical constraints of an fMEG system, it is not possible to use clinically established heart monitors for this purpose. Considering this constraint, we developed a real-time heart monitoring system for biomagnetic measurements and showed its reliability and applicability in research and for clinical examinations. The developed system consists of real-time access to fMEG data, an algorithm based on Independent Component Analysis (ICA), and a graphical user interface (GUI). The algorithm extracts the current fetal and maternal heart signal from a noisy and artifact-contaminated data stream in real-time and is able to adapt automatically to continuously varying environmental parameters. This algorithm has been named Adaptive Real-time ICA (ARICA) and is applicable to real-time artifact removal as well as to related blind signal separation problems. Index Terms--Adaptive, clustering, fetal, independent component analysis, magnetoencephalography, real-time systems.

A seven-channel SQUID system designed for acquiring both magnetoencephalography (MEG) and ultra-low-field magnetic resonance imaging (ULF MRI) data and also multi-channel 2-D image of whole human hand is described.

The studies related to the operation of a superconducting quantum interference device (SQUID) at a constant voltage coupled directly to the low noise preamplifier without any flux modulations are presented.

This paper presents evidence indicating that the signals generated by neural responses to visual input can be either enhanced by increasing or suppressed by decreasing the area of the stimuli to which attention is directed. We used magnetoencephalography (MEG) to measure the frequency-tagged steady-state visual evoked responses of 11 subjects presented with two superimposed images flickering at different frequencies. Each image consisted of seven parallel bars of equal length; in any image, all bars were either red or green and either horizontal or vertical. At randomly chosen times during the experiments, any one of the three middle bars in either image transiently increased or decreased in width. Subjects were asked to attend to one image and ignore the other and to respond to changes in bar width in the attended image with a key press. In one condition, subject responses were required for changes in any of the three central bars of the attended image. We found that visual steady-state evoked responses to the attended image were enhanced relative to those evoked by the unattended image in this condition, in a second condition, subject responses were required for changes only in the middle bar. In this condition, the responses to the attended image were suppressed relative to those of the unattended image. These results may reflect relative differences in the synchronization and desynchronization of responding neuronal populations.

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. Index Terms--Dynamics estimation, inverse problem, MEG/EEG, phase synchrony, single-trial analysis.

Dynamic systems have proven to be well suited to describe a broad spectrum of human coordination behavior such synchronization with auditory stimuli. Simultaneous measurements of the spatiotemporal dynamics of electroencephalographic (EEG) and magnetoencephalographic (MEG) data reveals that the dynamics of the brain signals is highly ordered and also accessible by dynamic systems theory. However, models of EEG and MEG dynamics have typically been formulated only in terms of phenomenological modeling such as fixed-current dipoles or spatial EEG and MEG patterns. In this paper, it is our goal to connect three levels of organization, that is the level of coordination behavior, the level of patterns observed in the EEG and MEG and the level of neuronal network dynamics. To do so, we develop a methodological framework, which defines the spatiotemporal dynamics of neural ensembles, the neural field, on a sphere in three dimensions. Using magnetic resonance imaging we map the neural field dynamics from the sphere onto the folded cortical surface of a hemisphere. The neural field represents the current flow perpendicular to the cortex and, thus, allows for the calculation of the electric potentials on the surface of the skull and the magnetic fields outside the skull to be measured by EEG and MEG, respectively. For demonstration of the dynamics, we present the propagation of activation at a single cortical site resulting from a transient input. Finally, a mapping between finger movement profile and EEG/MEG patterns is obtained using Volterra integrals. Index Terms--Dynamics, EEG, forward solution, MEG, modeling, network.

To reduce physiological artifacts in magnetoencephalographic (MEG) and electroencephalographic recordings, a number of methods have been applied in the past such as principal component analysis, signal-space projection, regression using secondary information, and independent component analysis. This method has become popular as it does not have constraints such as orthogonality between artifact and signal or the need for a priori information. Applying the time-delayed decorrelation algorithm to raw data from a visual stimulation MEG experiment, we show that several of the independent components can be attributed to the cardiac artifact. Calculating an average cardiac activity shows that physiologically different excitation states of the heart produce similar field distributions in the MEG sensor system. This is equivalent to differing spectral properties of cardiac field distributions in the raw data. As a consequence, the algorithm combines, e.g., the R peak and the T wave of the cardiac cycle into a single component and the one-to-one assignment of each independent component with a physiological source is not justified in this case. To improve the signal quality of visually evoked fields, the multidimensional cardiac artifact subspace is suppressed from the data. To assess the preservation of the evoked signal after artifact suppression, a geometrical and a temporal measure are introduced. The suppression of cardiac and [alpha] wave artifacts allows, in our experimental setting, the reduction of the number of epochs to one half while preserving the visually evoked signal. Index Terms--Alpha rhythm, cardiac artifact, independent component analysis, magnetoencephalography, superconducting quantum interference device (SQUID), temporal decorrelation, visually evoked fields.

A fast method for segmentation of a subject's skin, skull or brain compartment for electroencephalogram (EEG)/magnetoencephalogram (MEG) (E/MEG) source localization is proposed. The method is based on a description of volumes with sperical harmonics and a database of exact surfaces. Using the spherical harmonic coefficients, sets of basis surfaces are obtained for each compartment. New segmentations can be acquired by combining the appropriate basis surfaces to describe a delineation of the volume in a limited number of magnetic resonance (MR) slices. Alternatively, a representation of the skin can be derived from digitized head shape. Skull and brain then can be predicted from the skin representation with a prediction model also obtained from the segmentation database. Database segmentations were recomputed with the proposed method. Mean deviations from the originals were about 2 and 3 mm for compartments derived from MR and head shape. Dipole simulations with original surfaces for forward and computed segmentations for inverse calculations showed average dipole mislocalizations of 1.6 and 3.3 mm, respectively. With the proposed method highly accurate segmentation can be performed with much less effort and in much less time compared with other techniques. The method also is applicable when MR data is unavailable but a digitization of the head is. Index Terms--BEM,E/EMG source localization, MR image segmentation, spherical harmonics, SVD, volume conductor.

The Continuously Adjusted Least squares Method (CALM) of signal processing has been developed to achieve reduction in non-periodical low-frequency noise during magnetoencephalography measurements.

Magnetoencephalographic imaging is the estimation of three-dimensional neuronal current sources on the cortical surface from the measured magnetoencephalogram (MEG). It is a highly under-determined inverse problem as there are many 'feasible' images which are consistent with the MEG. Previous approaches to this problem have concentrated on the use of weighted minimum-norm inverse methods. These methods often produce overly smoothed images and exhibit bias towards surface sources. In this work we explore the maximum-entropy approach to obtain better solutions to the problem. This estimation technique selects, from the possible set of feasible images, the image with the highest entropy permitted by the available information. In order to account for the presence of noise in the data, we have also incorporated a noise rejection term into the maximum-entropy method (MEM). This makes our approach mirror a Bayesian maximum a posteriori probability (MAP) formulation. Additional information from other modalities like functional magnetic resonance imaging (fMRI) can also be incorporated into the proposed method in the form of a prior bias function to improve solutions. We demonstrate the method with experimental phantom data from a clinical 122-channel MEG system.

We describe a new approach to imaging neural current sources from measurements of the magnetoencephalogram (MEG) associated with sensory, motor, or cognitive brain activation. Many previous approaches to this problem have concentrated on the use of weighted minimum norm (WMN) inverse methods. While these methods ensure a unique solution, they do not introduce information specific to the MEG inverse problem, often producing overly smoothed solutions and exhibiting severe sensitivity to noise. We describe a Bayesian formulation of the inverse problem in which a Gibbs prior is constructed to reflect the sparse focal nature of neural current sources associated with evoked response data. We demonstrate the method with simulated and experimental phantom data, comparing its performance with several WMN methods. Index Terms - Bayesian imaging, magnetoencephalogram, mean field annealing, minimum norm.

A two-step method for identification and elimination of the cardiac contribution in single-trial magnetoencephalographic (MEG) signals is proposed. In the first step, the mean interfering signal (MIS) in one period is estimated by QRS-synchronous averaging of the raw MEG data. In the second step, a QRS-synchronous segmentation of the MEG signals is performed and each signal segment is Gram-Schmidt orthogonalized with the MIS. The above method is applied both to artificial and real MEG data. In each case the heart interference is all but eliminated whereas the components of interest, generated by the brain, remain almost unaffected. Index Terms - Interference supression, magnetoencephalography.

In this paper, we present a new approach to the recovering of dipole magnitudes in a distributed source model for magnetoencephalographic (MEG) and electroencephalographic (EEG) imaging. This method consists in introducing spatial and temporal a priori information as a cure to this ill-posed inverse problem. A nonlinear spatial regularization scheme allows the preservation of dipole moment discontinuities between some a priori noncorrelated sources, for instance, when considering dipoles located on both sides of a sulcus. Moreover, we introduce temporal smoothness constraints on dipole magnitude evolution at time scales smaller than those of cognitive processes. These priors are easily integrated into a Bayesian formalism, yielding a maximum a posteriori (MAP) estimator of brain electrical activity. Results from EEG simulations of our method are presented and compared with those of classical quadratic regularization and a now popular generalized minimum-norm technique called low-resolution electromagnetic tomography (LORETA). Index Terms - Bayesian imaging, EEG, edge-preserving regularization, MEG, multimodality imaging, source estimation.

In order to discriminate multiple sources of human hand somatosensory cortex we recorded somatosensory evoked field (SEF) after stimulation of three fingers (the thumb, fourth finger, and little finger), respectively. We applied time-frequency analysis (wavelet transforms) to examine the frequency component of each SEF data. Dominant power spectrum to the thumb (median nerve) stimulation appeared at higher frequency range than that of the little finger (ulna nerve) stimulation (thumb: 15-20 Hz, little finger: 10-15 Hz). In the result of fourth finger stimulation, the frequency of dominant power spectrum existed between that of the thumb and little finger stimulation. Furthermore, the coherence method was applied to these SEF data. Source localization was done by the SEF data to the fourth finger stimulation, having different frequency ranges (13-15 Hz and 16-18 Hz band-pass filter). We conclude that the proposed method, combining time-frequency analysis with the inverse problem of magnetoencephalogram, is useful for discriminating multiple sources of the SEF elicited by two nerve activities in the fourth finger overlapping in time. Index Terms--Magnetoencephalogram, somatosensory evoked field, SQUID, time-frequency analysis.

The present study attempted to specify the brain regions involved in a number comparison task by magnetoencephalography (MEG) measurement. Five normal subjects were required to see a pair of random digits displayed for 1.0 s and to judge which digit was larger. Neuromagnetic measurements were carried out with a whole-scalp 204 planar superconducting quantum device magnetometer. We estimated the location of single equivalent current dipoles (ECDs) in the comparison task. The ECDs related to the number comparison task were estimated in the vicinity of the superior parietal lobule of the left hemisphere at the latency of 254.8 [+ or -] 18.9 ms. Index Terms--Equivalent current dipole, magnetoencephalography, mental calculation, number comparison.

We propose a new group-theoretical approach to the problem of alignment of time events in multichannel signal recordings. Such an alignment is an essential phase in the classification of transients in electroencephalogram/magnetoencephalogram (MEG) signals. A common reference frame is reconstructed applying a time translation transformation based on delayed mutual correlation functions of the individual events. The method is applied to MEG data sets recorded from epileptic patients showing paroxysmal interictal discharges. Index Terms--Alignment, clustering, epilepsy, MEG, paroxysmal discharges.

Multichannel recordings of the electromagnetic fields emerging from neural currents in the brain generate large amounts of data. Suitable feature extraction methods are, therefore, useful to facilitate the representation and interpretation of the data. Recently developed independent component analysis (ICA) has been shown to be an efficient tool for artifact identification and extraction from electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings. In addition, ICA has been applied to the analysis of brain signals evoked by sensory stimuli. This paper reviews our recent results in this field. Index Terms--Independent component analysis (ICA), blind source separation (BSS), unsupervised learning, electroencephalography (EEG), magnetoencephalography(MEG), artifact removal, auditory evoked field (AEF), somatosensory evoked field (SEF).

To clarify the relationship between neuronal activity and the hemodynamic response, activations of the primary somatosensory cortex were studied using MEG(magnetoencepahlogram) and fMRI (functional magnetic resonance imaging). Effects of stimulus intensity by electrical stimulation to the right thumb was investigated. We focused on the number of activated pixels, time courses of hemodynamic response obtained by fMRI, and dipole moments of neuromagnetic activity. For fMRI experiments, a number of activated pixels and signal power, which was defined as the integrated value of MR signal, greatly increased when an increase in stimulus intensity generated pain. This trend, however, did not correspond to the observed MEG responses. When the stimulus intensity changed, fMRI responses showed the different tendency of MEG responses. Index terms - fMRI, MEG, signal power, somatosensory response