Equivalent dipole estimation of spontaneous EEG alpha activity: two-moving dipole approach

Spectral and spatial changes of brain rhythmic activity in response to the sustained thermal pain stimulation

The aim of this study was to investigate the neurophysiological correlates of pain caused by sustained thermal stimulation. A group of 21 healthy volunteers was studied. Sixty-four channel continuous electroencephalography (EEG) was recorded while the subject received tonic thermal stimulation. Spectral changes extracted from EEG were quantified and correlated with pain scales reported by subjects, the stimulation intensity, and the time course. Network connectivity was assessed to study the changes in connectivity patterns and strengths among brain regions that have been previously implicated in pain processing. Spectrally, a global reduction in power was observed in the lower spectral range, from delta to alpha, with the most marked changes in the alpha band. Spatially, the contralateral region of the somatosensory cortex, identified using source localization, was most responsive to stimulation status. Maximal desynchrony was observed when stimulation was present. The degree of alpha power reduction was linearly correlated to the pain rating reported by the subjects. Contralateral alpha power changes appeared to be a robust correlate of pain intensity experienced by the subjects. Granger causality analysis showed changes in network level connectivity among pain-related brain regions due to high intensity of pain stimulation versus innocuous warm stimulation. These results imply the possibility of using noninvasive EEG to predict pain intensity and to study the underlying pain processing mechanism in coping with prolonged painful experiences. Once validated in a broader population, the present EEG-based approach may provide an objective measure for better pain management in clinical applications. Hum Brain Mapp 37:2976-2991, 2016. © 2016 Wiley Periodicals, Inc.

Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms

Brain-computer interfaces (BCIs) have been explored in the field of neuroengineering to investigate how the brain can use these systems to control external devices. We review the principles and approaches we have taken to develop a sensorimotor rhythm EEG based brain-computer interface (BCI). The methods include developing BCI systems incorporating the control of physical devices to increase user engagement, improving BCI systems by inversely mapping scalp-recorded EEG signals to the cortical source domain, integrating BCI with noninvasive neuromodulation strategies to improve learning, and incorporating mind-body awareness training to enhance BCI learning and performance. The challenges and merits of these strategies are discussed, together with recent findings. Our work indicates that the sensorimotor-rhythm-based noninvasive BCI has the potential to provide communication and control capabilities as an alternative to physiological motor pathways.

A review of EEG and MEG for brainnetome research

The majority of brain activities are performed by functionally integrating separate regions of the brain. Therefore, the synchronous operation of the brain's multiple regions or neuronal assemblies can be represented as a network with nodes that are interconnected by links. Because of the complexity of brain interactions and their varying effects at different levels of complexity, one of the corresponding authors of this paper recently proposed the brainnetome as a new -ome to explore and integrate the brain network at different scales. Because electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive and have outstanding temporal resolution and because they are the primary clinical techniques used to capture the dynamics of neuronal connections, they lend themselves to the analysis of the neural networks comprising the brainnetome. Because of EEG/MEG's applicability to brainnetome analyses, the aim of this review is to identify the procedures that can be used to form a network using EEG/MEG data in sensor or source space and to promote EEG/MEG network analysis for either neuroscience or clinical applications. To accomplish this aim, we show the relationship of the brainnetome to brain networks at the macroscale and provide a systematic review of network construction using EEG and MEG. Some potential applications of the EEG/MEG brainnetome are to use newly developed methods to associate the properties of a brainnetome with indices of cognition or disease conditions. Associations based on EEG/MEG brainnetome analysis may improve the comprehension of the functioning of the brain in neuroscience research or the recognition of abnormal patterns in neurological disease.

Electrophysiological imaging of brain activity and connectivity-challenges and opportunities

Unlocking the dynamic inner workings of the brain continues to remain a grand challenge of the 21st century. To this end, functional neuroimaging modalities represent an outstanding approach to better understand the mechanisms of both normal and abnormal brain functions. The ability to image brain function with ever increasing spatial and temporal resolution has made a significant leap over the past several decades. Further delineation of functional networks could lead to improved understanding of brain function in both normal and diseased states. This paper reviews recent advancements and current challenges in dynamic functional neuroimaging techniques, including electrophysiological source imaging, multimodal neuroimaging integrating fMRI with EEG/MEG, and functional connectivity imaging.

EEG-fMRI reciprocal functional neuroimaging

OBJECTIVE

Integration of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) has been pursued in an effort to achieve greater spatio-temporal resolution of imaging dynamic brain activity. We report a data-driven approach to image spatio-temporal features of neural oscillatory activity and event-related activity from continuously recorded EEG and fMRI signals.

METHODS

This approach starts with using the independent component analysis (ICA) to decompose the spatio-temporal EEG data into a linear combination of scalp potential maps and time courses. The time course of each independent component (IC) is used to construct a regressor to fit the fMRI time series. The resultant fMRI map then feeds back as a spatial constraint to the estimation of the source distribution underlying the corresponding IC's scalp map. The estimated source distributions multiplied by the corresponding IC time courses are summed across all ICs, giving rise to the reconstructed spatio-temporal brain activity. Functional connectivity between cortical areas can be further revealed from the imaged source signals using phase synchrony measures. We tested the method using both simulated oscillatory activity and event-related neural activity at various cortical regions. We also used this method to study the alpha-band EEG modulations in an eyes-open-eyes-closed human experiment.

RESULTS

In the simulation study, reliable reconstruction of the localization, time-frequency feature and cortical functional connection were achieved for the simulated oscillatory and event-related activities. In the experimental study, the alpha rhythmic modulation was localized mainly in the occipital visual area and the parieto-occipital sulcus. Within these regions, time-frequency analysis and phase-synchronization analysis indicated increased alpha power and alpha-band phase-synchronization in eyes-closed condition versus eyes-open condition.

CONCLUSION

Our results suggest that the proposed approach is well suited to image continuously oscillatory activities and their functional connectivity.

SIGNIFICANCE

Such ability promises to facilitate the investigation of the long-term neural behaviors and large-scale cortical interactions involved in spontaneous brain activity and cognitive tasks.

Spatio-temporal EEG dipole estimation by means of a hybrid genetic algorithm

EEG source localization can be considered as a nonlinear optimization process. In the present study, a hybrid genetic algorithm (HGA) is introduced, which combines genetic and local search strategies to overcome the disadvantages of conventional genetic algorithm and local optimization methods. This HGA algorithm was used to localize two dipoles from scalp EEG, and yielded localization accuracy range of 0.95 cm-1.55 cm when the noise level is within 15%, which is better than the Simplex and GA algorithms in localizing multiple dipoles.

Estimation of number of independent brain electric sources from the scalp EEGs

In electromagnetic source analysis, many source localization strategies require the number of sources as an input parameter (e.g., spatio-temporal dipole fitting and the multiple signal classification). In the present study, an information criterion method, in which the penalty functions are selected based on the spatio-temporal source model, has been developed to estimate the number of independent dipole sources from electromagnetic measurements such as the electroencephalogram (EEG). Computer simulations were conducted to evaluate the effects of various parameters on the estimation of the source number. A three-concentric-spheres head model was used to approximate the head volume conductor. Three kinds of typical signal sources, i.e., the damped sinusoid sources, sinusoid sources with one frequency band and sinusoid sources with two separated frequency bands, were used to simulate the oscillation characteristics of brain electric sources. The simulation results suggest that the present method can provide a good estimate of the number of independent dipole sources from the EEG measurements. In addition, the present simulation results suggest that choosing the optimal penalty function can successfully reduce the effect of noise on the estimation of number of independent sources. The present study suggests that the information criterion method may provide a useful means in estimating the number of independent brain electrical sources from EEG/MEG measurements.

On the estimation of the number of dipole sources in EEG source localization

OBJECTIVE

The purpose of the present study was to determine the number of the equivalent dipole sources corresponding to the scalp EEG using the information criterion method based on the instantaneous-state modeling.

METHODS

A three-concentric-spheres head model was used to represent the head volume conductor. The Powell algorithm was used to solve the inverse problem of estimating the equivalent dipoles from the scalp EEG. The information criterion with different penalty functions was used to determine the dipole number. Computer simulations were conducted to evaluate effects of various parameters on the estimation of dipole number.

RESULTS

The present results suggest that the present method is able to estimate the number of equivalent current dipoles (ECDs) from instantaneous scalp EEG measurements, and that increase in the electrode number can improve the accuracy of estimation of the ECD number. For two ECDs, the best performance of estimation with 20% white noise were 85%, 92% and 94%, when 64, 128 and 256 electrodes are used, respectively. When there are 3 ECDs, the present results suggest that using 256 electrodes gave up to 82% estimation accuracy. The present simulation results also indicate that the accuracies of identification are similar when the minimum distance between dipoles is either 1 or 2 cm, which was used in the simulation. It was also found that the different penalty functions used in the information criterion method could have substantial influence on the estimation accuracy.

CONCLUSIONS

The present method can estimate the number of ECDs from instantaneous scalp EEG distribution for up to three dipoles.

SIGNIFICANCE

The successful estimation of the number of ECDs will play an important role in expanding the applicability of dipole source localization to multiple sources.

Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis

We have developed a novel approach using source analysis for classifying motor imagery tasks. Two-equivalent-dipoles analysis was proposed to aid classification of motor imagery tasks for brain-computer interface (BCI) applications. By solving the electroencephalography (EEG) inverse problem of single trial data, it is found that the source analysis approach can aid classification of motor imagination of left- or right-hand movement without training. In four human subjects, an averaged accuracy of classification of 80% was achieved. The present study suggests the merits and feasibility of applying EEG inverse solutions to BCI applications from noninvasive EEG recordings.

An alternative subspace approach to EEG dipole source localization

In the present study, we investigate a new approach to electroencephalography (EEG) three-dimensional (3D) dipole source localization by using a non-recursive subspace algorithm called FINES. In estimating source dipole locations, the present approach employs projections onto a subspace spanned by a small set of particular vectors (FINES vector set) in the estimated noise-only subspace instead of the entire estimated noise-only subspace in the case of classic MUSIC. The subspace spanned by this vector set is, in the sense of principal angle, closest to the subspace spanned by the array manifold associated with a particular brain region. By incorporating knowledge of the array manifold in identifying FINES vector sets in the estimated noise-only subspace for different brain regions, the present approach is able to estimate sources with enhanced accuracy and spatial resolution, thus enhancing the capability of resolving closely spaced sources and reducing estimation errors. The present computer simulations show, in EEG 3D dipole source localization, that compared to classic MUSIC, FINES has (1) better resolvability of two closely spaced dipolar sources and (2) better estimation accuracy of source locations. In comparison with RAP-MUSIC, FINES' performance is also better for the cases studied when the noise level is high and/or correlations among dipole sources exist.

A computer simulation study of cortical imaging from scalp potentials

In this paper, computer simulation studies were conducted to test the feasibility of imaging brain electrical activity from the scalp electroencephalograms. The inhomogeneous three-concentric-sphere head model was used to represent the head volume conductor. Closed spherical dipole layers, consisting of several thousand uniformly distributed dipoles, were used to reconstruct the cortical potential maps corresponding to neuronal sources located inside the brain. Simulation results indicate that the present procedure can image both cortical and deep sources, and for the cortical sources, a spatial resolution as high as 1.2 cm can be achieved.

Multichannel EEG fields during and without visual input: frequency domain model source locations and dimensional complexities

27-Channel EEG potential map series were recorded from 12 normals with closed and open eyes. Intracerebral dipole model source locations in the frequency domain were computed. Eye opening (visual input) caused centralization (convergence and elevation) of the source locations of the seven frequency bands, indicative of generalized activity; especially, there was clear anteriorization of alpha-2 (10.5-12 Hz) and beta-2 (18.5-21 Hz) sources (alpha-2 also to the left). Complexity of the map series' trajectories in state space (assessed by Global Dimensional Complexity and Global OMEGA Complexity) increased significantly with eye opening, indicative of more independent, parallel, active processes. Contrary to PET and fMRI, these results suggest that brain activity is more distributed and independent during visual input than after eye closing (when it is more localized and more posterior).

An equivalent body surface charge model representing three-dimensional bioelectrical activity

A new surface-source model has been developed to account for the bioelectrical potential on the body surface. A single-layer surface-charge model on the body surface has been developed to equivalently represent bioelectrical sources inside the body. The boundary conditions on the body surface are discussed in relation to the surface-charge in a half-space conductive medium. The equivalent body surface-charge is shown to be proportional to the normal component of the electric field on the body surface just outside the body. The spatial resolution of the equivalent surface-charge distribution appears intermediate between those of the body surface potential distribution and the body surface Laplacian distribution. An analytic relationship between the equivalent surface-charge and the surface Laplacian of the potential was found for a half-space conductive medium. The effects of finite spatial sampling and noise on the reconstruction of the equivalent surface-charge were evaluated by computer simulations. It was found through computer simulations that the reconstruction of the equivalent body surface-charge from the body surface Laplacian distribution is very stable against noise and finite spatial sampling. The present results suggest that the equivalent body surface-charge model may provide an additional insight to our understanding of bioelectric phenomena.

DSL and MUSIC under model misspecification and noise-conditions

The concepts of both the traditional DSL (Dipole Source Localization) and MUSIC (MUltiple SIgnal Classification) are explained using simple vectors. DSL and MUSIC differ in the way the weighting functions are found. In both DSL and MUSIC, the fitted generator magnitudes are found by projecting the recorded potential map onto the fitted weighting functions, and the model transfers the weighting functions into generator locations/orientations. Both DSL and MUSIC will cause errors in the fitted dipole parameters when there is model misspecification, noise, or both. Therefore, an accurate head model is essential for either method to give reliable results.

Data-determined window size and space-oriented segmentation of spontaneous EEG map series

For the segmentation of series of momentary potential distribution maps into epochs of quasi-stable landscape (brain electric microstates), the maps are reduced to extracted landscape descriptors. Changes of the descriptors over time are recognized as segment terminators. The selection of the descriptors' tolerated variance (the window size) determines the result. We present a window-determining function which allows a data-driven determination of the optimal window size, based on equal weight given to the recognition of similarity and dissimilarity between maps. Segmentations based on two map descriptors (locations of extreme potentials and centroids) were used on 211 two-second map epochs from 8 normal subjects for validation of the window-determining function and to establish normative data. Using the data-determined window sizes for segmentation, the mean duration of the obtained microstates across subjects did not differ between descriptors (144 and 143 msec, respectively). Random permutation of the maps in time produced significantly shorter segments, ensuring that the segmentation disclosed real properties of the original data and not artifacts of the procedure.