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Electromagnetic source imaging (ESI) techniques have become one of the most common alternatives for understanding cognitive processes in the human brain and for guiding possible therapies for neurological diseases. However, ESI accuracy strongly depends on the forward model capabilities to accurately describe the subject's head anatomy from the available structural data. Attempting to improve the ESI performance, we enhance the brain structure model within the individual-defined forward problem formulation, combining the head geometry complexity of the modeled tissue compartments and the prior knowledge of the brain tissue morphology. We validate the proposed methodology using 25 subjects, from which a set of magnetic-resonance imaging scans is acquired, extracting the anatomical priors and an electroencephalography signal set needed for validating the ESI scenarios. Obtained results confirm that incorporating patient-specific head models enhances the performed accuracy and improves the localization of focal and deep sources., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2020 Yohan Céspedes-Villar et al.)

In the recent past, estimating brain activity with magneto/electroencephalography (M/EEG) has been increasingly employed as a noninvasive technique for understanding the brain functions and neural dynamics. However, one of the main open problems when dealing with M/EEG data is its non-Gaussian and nonstationary structure. In this paper, we introduce a methodology for enhancing the data covariance estimation using a weighted combination of multiple Gaussian kernels, termed WM-MK, that relies on the Kullback-Leibler divergence for associating each kernel weight to its relevance. From the obtained results of validation on nonstationary and non-Gaussian brain activity (simulated and real-world EEG data), WM-MK proves that the accuracy of the source estimation raises by more effectively exploiting the measured nonlinear structures with high time and space complexity.

Accurate source localization of electroencephalographic (EEG) signals requires detailed information about the geometry and physical properties of head tissues. Indeed, these strongly influence the propagation of neural activity from the brain to the sensors. Finite difference methods (FDMs) are head modelling approaches relying on volumetric data information, which can be directly obtained using magnetic resonance (MR) imaging. The specific goal of this study is to develop a computationally efficient FDM solution that can flexibly integrate voxel-wise conductivity and anisotropy information. Given the high computational complexity of FDMs, we pay particular attention to attain a very low numerical error, as evaluated using exact analytical solutions for spherical volume conductor models. We then demonstrate the computational efficiency of our FDM numerical solver, by comparing it with alternative solutions. Finally, we apply the developed head modelling tool to high-resolution MR images from a real experimental subject, to demonstrate the potential added value of incorporating detailed voxel-wise conductivity and anisotropy information. Our results clearly show that the developed FDM can contribute to a more precise head modelling, and therefore to a more reliable use of EEG as a brain imaging tool.

We present a novel iterative regularized algorithm (IRA) for neural activity reconstruction that explicitly includes spatiotemporal constraints, performing a trade-off between space and time resolutions. For improving the spatial accuracy provided by electroencephalography (EEG) signals, we explore a basis set that describes the smooth, localized areas of potentially active brain regions. In turn, we enhance the time resolution by adding the Markovian assumption for brain activity estimation at each time period. Moreover, to deal with applications that have either distributed or localized neural activity, the spatiotemporal constraints are expressed through [Formula: see text] and [Formula: see text] norms, respectively. For the purpose of validation, we estimate the neural reconstruction performance in time and space separately. Experimental testing is carried out on artificial data, simulating stationary and non-stationary EEG signals. Also, validation is accomplished on two real-world databases, one holding Evoked Potentials and another with EEG data of focal epilepsy. Moreover, responses of functional magnetic resonance imaging for the former EEG data have been measured in advance, allowing to contrast our findings. Obtained results show that the [Formula: see text]-based IRA produces a spatial resolution that is comparable to the one achieved by some widely used sparse-based estimators of brain activity. At the same time, the [Formula: see text]-based IRA outperforms other similar smooth solutions, providing a spatial resolution that is lower than the sparse [Formula: see text]-based solution. As a result, the proposed IRA is a promising method for improving the accuracy of brain activity reconstruction.

We study the influence of the anisotropic white matter within the ElectroEncephaloGraphy source localization problem. To this end, we consider three cases of the anisotropic white matter modeled in two concrete cases: by fixed or variable ratio. We extract information about highly anisotropic areas of the white matter from real Diffusion Weighted Imaging data. To validate the compared anisotropic models, we introduce the localization dipole and orientation errors. Obtained results show that the white matter model with a fixed anisotropic ratio leads to values of dipole localization error close to 1cm and may be enough in those cases avoiding localized analysis of neural brain activity. In contrast, modeling based on the anisotropic variable rate assumption becomes important in tasks regarding analysis and localization of deep sources neighboring the white matter tissue.

We study the influence of different conductivity models within the framework of electroencephalogram (EEG) source localization on the white matter and skull areas. Particularly, we investigate five different spherical models having either isotropic or anisotropic conductivity for both considered areas. To this end, the anisotropic finite difference reciprocity method is used for solving the EEG forward problem. We evaluate a model of a numeric skull conductivity in terms of the minimum dipole localization/orientation error. As a result, both considered models of the skull reach the lowest dipole localization error (less than 6 mm), namely: i) single anisotropic layer and ii) three isotropic layers (hard bone/spongy bone/hard bone). Additionally, two different electrode configurations (10-20 and 10-10 systems) are tested showing that the error decreases almost as much as twice for the latter one though the computational burden significantly increases.

Recently, there have been many efforts to develop Brain Computer Interface (BCI) systems, allowing identifying and discriminating brain activity, as well as, support the control of external devices, and to understand cognitive behaviors. In this work, a feature relevance analysis approach based on an eigen decomposition method is proposed to support automatic Motor Imagery (MI) discrimination in electroencephalography signals for BCI systems. We select a set of features representing the best as possible the studied process. For such purpose, a variability study is performed based on traditional Principal Component Analysis. EEG signals modelling is carried out by feature estimation of three frequency-based and one time-based. Our approach provides testing over a well-known MI dataset. Attained results show that presented algorithm can be used as tool to support discrimination of MI brain activity, obtaining acceptable results in comparison to state of the art approaches.

Time-frequency decompositions (TFDs) are well known techniques that permit to extract useful information or features from EEG signals, being necessary to distinguish between irrelevant information and the features effectively representing the subjacent physiological phenomena, according to some evaluation measure. This work introduces a new method to obtain relevant features extracted from time-frequency plane for epileptic EEG signals. Particularly, EEG features are extracted by common spectral methods such as short time Fourier transform (STFT), wavelets transform and Empirical Mode Decomposition (EMD). Then, each method is evaluated by Stochastic Relevance Analysis (SRA) that is further used for EEG classification and channel selection. The classification measures are carried out based on the performance of the k-NN classifier, while the channels selected are validated by visual inspection and topographic scalp map. The study uses real and multi-channel EEG data and all the experiments have been supervised by an expert neurologist. Results obtained in this paper show that SRA is a good alternative for automatic seizure detection and also opens the possibility of formulating new criteria to select, classify or analyze abnormal EEG channels.

This paper is focused on testing the latency contribution as regards the quality of formed groups for discriminating between healthy and attention deficit hyperactivity disorder children. To this end, two different cases are considered: nonaligned original recordings and aligned signals according to P300 position. For latter case, a novel approach to conduct time location of P300 component is introduced, which is based on derivative of event-related potential signals. The used database holds event-related potentials registered in auditory and visual oddball paradigm. Several experiments are carried out testing both configurations of considered data matrix. For grouping input data matrices, the k-means clustering technique is employed. To assess the quality of formed clusters and the relevance for clustering of latency-based features, relative values of distances between centroids and data points are computed in order to apprise separability and compactness of estimated clusters. Experimental results show that time localization of P300 component is not a decisive feature in formation of compact and well-defined groups within a discrimination framework for two considered data classes under certain conditions.

Biosignal recordings are widely used in the medical environment to support the evaluation and the diagnosis of pathologies. Nevertheless, the main difficulty lies in the non-stationary behavior of the biosignals, difficulting the obtention of patterns characterizing the changes in physiological or pathological states. Thus, the obtention of the stationary and non-stationary components of a biosignal is still an open issue. This work proposes a methodology to detect time-homogeneities based on time-frequency analysis with aim to extract the non-stationary behavior of the biosignal. Results show an increase in the stationarity and in the distance between classes of the reconstructions from the enhanced time-frequency representations. The stationary components extracted with the proposed approach can be used to solve biosignal classification problems.

Microelectrode recordings are a valuable tool for assisting localization targets during deep brain stimulation procedures in Parkinson's disease neurosurgery. Attempts to automate and standardize this process have been limited by variability in patient neurophysiology and strong dynamics of microelectrode recordings. In this paper, a methodology for the identification of basal ganglia nuclei is presented that is based on a signal-dependent filter bank method using microelectrode recordings. The method is a customized realization of the discrete wavelet transform via the lifting scheme that is optimally tuned by genetic algorithms. Using this method, unique mother wavelet functions that exhibit an adaptable spectrum to the microelectrode recording dynamic are generated. Additionally, by extracting morphological features from the space-transformed microelectrode recording, it is possible to integrate them into three-dimensional (3D) feature spaces with maximum class separability. Finally, high discriminant feature spaces are fed into basic classifiers to recognize up to four basal nuclei. Comparison with several existing wavelets highlights the characteristics of new mother wavelets. Additionally, classification results show that identification of addressed nuclei in the basal ganglia can be performed with 95% confidence.

A methodology for wavelet synthesis based on lifting scheme and genetic algorithms is presented. Often, the wavelet synthesis is addressed to solve the problem of choosing properly a wavelet function from an existing library, but which may be not specially designed to the application in hand. The task under consideration is the identification of epileptic seizures over electroencephalogram recordings. Although basic classifiers are employed, results rendered that the proposed methodology is successful in the considered study achieving similar classification rates that had been reported in literature.

This paper presents a new method to estimate dynamic neural activity from EEG signals. The method is based on a Kalman filter approach, using physiological models that take both spatial and temporal dynamics into account. The filter's performance (in terms of estimation error) is analyzed for the cases of linear and nonlinear models having either time invariant or time varying parameters. The best performance is achieved with a nonlinear model with time-varying parameters.

We introduce a novel approach to source reconstruction from electroencephalographic (EEG) recordings, introducing physiologically motivated spatio-temporal constraints over the source representation. Within a Dynamic Sparse Coding (DSC) formulation, we promote the source reconstruction to encode the strong temporal dynamics of EEG data, enabling the analysis of smooth and localized characteristics in space. Consequently, by adjusting the ratio between a couple of included regularization terms, our method enables reaching a trade-off between temporal and spatial resolution, which can be ruled depending on each specific analysis and provided data. We validate the method performance on simulated event-related potentials, fixing a distinct number of active sources and under different values of signal-to-noise ratio. Also, testing is performed on real-world EEG data related to emotion brain activity, for which the obtained results prove that the most active sources originate in cortical areas related to visual and attention processes, as well as in regions involved in emotional processing. Additional comparison with the state-of-the-art methods is also performed for simulated and real EEG signals. As a result, the proposed DSC based solution enhances the EEG characterization of source activity, particularly, when dealing with strong temporal dynamics. [ABSTRACT FROM AUTHOR]

The use of motor imagery discrimination using feature relevance analysis (MIDFR) is investigated for classification tasks based on electroencephalography (EEG) signals. The method addresses the problem of a direct and automatic finding of the time-varying features influencing the most on distinguishing motor imagery tasks. The method introduces a stochastic relevance stage that is primarily used for properly handling the set of short-time features, which are extracted as to make prominent the nonstationary behavior of the EEG data. Furthermore, since it is widely accepted that the motor imagery information is concentrated in the μ and β neural activity bands, we make use of the empirical mode decomposition together with the common-spatial-patterns mapping. We test two different motor imagery databases, using a soft-margin support vector machine classifier that is validated by a 10-fold cross-validation methodology. Since the proposed MIDFR algorithm better encodes neural activity dynamics, experimental results carried out, which are also contrasted with other state-of-the art approaches, show that the proposed approach allows improving detection of MI classification tasks. Besides, the computed relevance on the EEG channels is in accordance with other clinical findings reported in the literature. [ABSTRACT FROM AUTHOR]