Guoya Dong

Standardized shrinking LORETA-FOCUSS (SSLOFO): a new algorithm for spatio-temporal EEG source reconstruction

This paper presents a new algorithm called Standardized Shrinking LORETA-FOCUSS (SSLOFO) for solving the electroencephalogram (EEG) inverse problem. Multiple techniques are combined in a single procedure to robustly reconstruct the underlying source distribution with high spatial resolution. This algorithm uses a recursive process which takes the smooth estimate of sLORETA as initialization and then employs the re-weighted minimum norm introduced by FOCUSS. An important technique called standardization is involved in the recursive process to enhance the localization ability. The algorithm is further improved by automatically adjusting the source space according to the estimate of the previous step, and by the inclusion of temporal information. Simulation studies are carried out on both spherical and realistic head models. The algorithm achieves very good localization ability on noise-free data. It is capable of recovering complex source configurations with arbitrary shapes and can produce high quality images of extended source distributions. We also characterized the performance with noisy data in a realistic head model. An important feature of this algorithm is that the temporal waveforms are clearly reconstructed, even for closely spaced sources. This provides a convenient way to estimate neural dynamics directly from the cortical sources.

The comparison between FVM and FEM for EIT forward problem

In this paper, the finite volume method (FVM) is introduced in detail for solving the electrical impedance tomography (EIT) forward problem. A new idea for constructing the primary and secondary elements in FVM is presented. Detailed comparisons between FVM and the finite element method (FEM), including the characteristic of the coefficient matrix and the precision of the results, are carried out under the same mesh system. It is shown that accurate estimates of the potential distribution can be obtained with an FVM solution.

Transient impact of spike on theta rhythm in temporal lobe epilepsy.

Epileptic spike is an indicator of hyper-excitability and hyper-synchrony of neural networks. While cognitive deficit in epilepsy is a common observation, how spikes transiently influence brain oscillations, especially those essential for cognitive functions, remains obscure. Here we aimed to quantify the transient impacts of sporadic spikes on theta oscillations and investigate how such impacts may evolve during epileptogenesis. Longitudinal depth EEG data were recorded in the CA1 area of pilocarpine temporal lobe epilepsy (TLE) rat models. Phase stability, a measure of synchrony, and theta power were estimated around spikes as well as in the protracted spike-free periods (FP) at least 1h after spike bursts. We found that the change in theta power did not correlate with the change in phase stability. More importantly, the impact of spikes on theta rhythm was highly time-dependent. While theta power decreased abruptly after spikes both in the latent and chronic stages, changes of theta phase stability demonstrated opposite trends in the latent and chronic stages, potentially due to the substantial reorganization of neural circuits along epileptogenesis. During FP, theta phase stability was significantly higher than the baseline level before injections, indicating that hyper-synchrony remained even hours after the spike bursts. We concluded that spikes have transient negative effects on theta rhythm, however, impacts are different during latent and chronic stages, implying that its influence on cognitive processes may also change over time during epileptogenesis.

The application of the generalized vector sample pattern matching method for EIT image reconstruction

This paper presents a new application of a generalized vector sample pattern matching (GVSPM) method for image reconstruction of conductivity changes in electrical impedance tomography. GVSPM is an iterative method for linear inverse problems. The key concept of the GVSPM is that the objective function is defined in terms of an angular component between the inner product of the known vector and solution of a system of equations. Comparisons are presented between images of simulated and experimental data, reconstructed using truncated singular value decomposition and GVSPM. In both cases, a normalized sensitivity matrix is constructed using the finite volume method to solve the forward problem.

Classifying the multiplicity of the EEG source models using sphere-shaped support vector Machines

Support vector machines (SVMs) are learning algorithms derived from statistical learning theory, and originally designed to solve binary classification problems. How to effectively extend SVMs for multiclass classification problems is still an ongoing research issue. In this paper, a sphere-shaped SVM for multiclass problems is presented. Compared with the classical plane-shaped SVMs, the number of convex quadratic programming problems and the number of variables in each programming are smaller. Such SVM classifier is applied to the electroencephalogram (EEG) source localization problem, and the multiplicity of source models is determined according to the potentials recorded on the scalp. Experimental results indicate that the sphere-shaped SVM based classifier is an effective and promising approach for this task.

Single trial EEG classification during finger movement task by using hidden Markov models

A new algorithm based on hidden Markov models (HMM) to discriminate single trial electroencephalogram (EEG) between two conditions of finger movement task is proposed. Firstly, multi-channel EEG signals of single trial are filtered in both frequency and spatial domains. The pass bands of the two filters in frequency domain are 0~3 Hz and 8~30 Hz respectively, and the spatial filters are designed by the methods of common spatial subspace decomposition (CSSD). Secondly, two independent features are extracted based on HMM. Finally, the movement tasks are classified into two groups by a perceptron with the extracted features as inputs. With a leave-one out training and testing procedure, an average classification accuracy rate of 93.2% is obtained based on the data from five subjects. The proposed method can be used as an EEG-based brain computer interface (BCI) due to its high recognition rate and insensitivity to noise. In addition, it is suitable for either offline or online EEG analysis

GVSPM for reconstruction in electrical impedance tomography

We apply the generalized vector sampled pattern matching (GVSPM) method to an inverse parameter problem for the reconstruction of electrical impedance tomography (EIT). Most of the inverse problems are reduced into solving for an ill-posed system of equations whose solution is not uniquely determined. The GVSPM introduced in this paper enables us to select the physically existing solution among possible ones. By applying GVSPM for EIT reconstruction, this point is verified by reliable reconstructed images.

EIT Images with Improved Spatial Resolution Using a Realistic Head Model

A recursive algorithm is presented to improve the spatial resolution of 3-D Electrical Impedance Tomography (EIT) images in a four-shell realistic head model. In this algorithm, the low spatial resolution image derived from the standardized low resolution electromagnetic tomography algorithm (sLORETA) is chosen to be the initial estimate for the Focal Underdetermined System Solver (FOCUSS), and a shrinking strategy is adopted for adjusting the source space during iteration process in FOCUSS. Images are presented with improved spatial resolution and the algorithm effectiveness is verified on simulated data by setting two perturbations in the movement and visual regions of the brain

A theoretical computation of abnormal oscillation propagation in a 2-D excitable neuronal network coupled via gap junction

The propagation of abnormal oscillations in actual neural tissue is often irregular and highly complex. The experiments and theoretical work on it are both very difficult; however, it can be helpful to understand some disorder in the neural system. With the help of microelectrode recording techniques and microdialysis, some experimental results from human beings and animal models have demonstrated that epileptic seizures can occur when either the external cellular environment of neurons is changed drastically from physiological conditions or when the synapses of neurons are extensively induced to release neurotransmitter or other neural signals. Here, we present a theoretical framework to investigate the gap junctions (electrical synapse) effect on the propagation of abnormal oscillations. Although such theoretical work is still very limited in explaining all the mechanistic problems related to the disorder situation, e.g., epilepsy, it is, nevertheless, helpful to our understanding of synaptic effects on the abnormal activity propagation. Now, from ionic channels to neural networks, a two-dimensional (2-D) spatial-temporal partial differential equation (PDE) is built. The implicit scheme of the finite-differential method in the time domain and a multistep algorithm are utilized to solve the PDE and the nonlinear ordinary differential equations, respectively, while the successive overrelaxation method is utilized to compute the large-scale sparse equations. Lyapunov exponent and approximate entropy are further applied, respectively, to the analysis of chaos and complexity in the propagation. Numerical results show that abnormal oscillations can propagate when the coupling strength of the gap junctions is sufficiently large, leading to turbulence in the excitable network, and that the larger the coupling strength is, the greater the nonlinear and the complexity of the propagation are. It is also concluded that the chaos and the complexity of the activity at the periphery point are larger than that at the central point when the abnormal oscillations propagate from the central to the periphery. This theoretical work is helpful to understand the gap junctions effects on the abnormal oscillation propagation in a 2-D excitable neural tissue.

Spatial resolution improvement of 3D EIT images by the shrinking sLORETA-FOCUSS algorithm

This paper describes the use of the shrinking sLORETA-FOCUSS algorithm to improve the spatial resolution of three-dimensional (3D) EIT images. Conventional EIT yields inaccurate, low spatial resolution images, due to noise, the low sensitivity of boundary voltages to inner conductivity perturbations and a limited number of boundary voltage measurements. The focal underdetermined system solver (FOCUSS) algorithm produces a localized energy solution based on the weighted minimum-norm least-squares (MNLS) solution. It was successfully applied for the spatial resolution improvement of EIT images of simulated and tank data for a 2D homogeneous circular disc. However, due to the fact that a 3D mesh system contains many more elements, much more memory is required to store the weighting matrix. In order to extend the work to 3D, the shrinking-FOCUSS method is utilized to shrink the solution space as well as the weighting matrix in each iteration step. The solution of the standardized low resolution electromagnetic tomography algorithm (sLORETA) is adopted as the initial estimate of the shrinking-FOCUSS. The effectiveness is verified by implementing the new algorithm on tank data for a three-dimensional homogeneous sphere.