Michalis Zervakis

Review on solving the inverse problem in EEG source analysis

In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided.This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF), SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF, for different noise levels and different simulated source depths has shown that for single source localization, regularized sLORETA gives the best solution in terms of both localization error and ghost sources. Furthermore the computationally intensive solution given by SLF was not found to give any additional benefits under such simulated conditions.

A survey on industrial vision systems, applications and tools

The state of the art in machine vision inspection and a critical overview of real-world applications are presented in this paper. Two independent ways to classify applications are proposed, one according to the inspected features of the industrial product or process and the other according to the inspection independent characteristics of the inspected product or process. The most contemporary software and hardware tools for developing industrial vision systems are reviewed. Finally, under the light of recent advances in image sensors, software and hardware technology, important issues and directions for designing and developing industrial vision systems are identified and discussed

Detection and segmentation of drusen deposits on human retina: potential in the diagnosis of age-related macular degeneration.

Assessment of the risk for the development of age-related macular degeneration requires reliable detection and quantitative mapping of retinal abnormalities that are considered as precursors of the disease. Typical signs for the latter are the so-called drusen that appear as abnormal white-yellow deposits on the retina. Segmentation of these features using conventional image analysis methods is quite complicated mainly due to the non-uniform illumination and the variability of the pigmentation of the background tissue. This paper presents a novel segmentation algorithm for the automatic detection and mapping of drusen in retina images acquired with the aid of a digital Fundus camera. We employ a modified adaptive histogram equalization, namely the multilevel histogram equalization (MLE) scheme, for enhancing local intensity structures. For the detection of drusen in retina images, we develop a novel segmentation technique, the histogram-based adaptive local thresholding (HALT), which extracts the useful information from an image without being affected by the presence of other structures. We provide experimental results from the application of our technique to real images, where certain abnormalities (drusen) have slightly different characteristics from the background. The performance of the algorithm is established through statistical analysis of the results. This analysis indicates that the proposed drusen detector gives reliable detection accuracy in both position and mass size.

Automated analysis of FISH and immunohistochemistry images: A review

Fluorescent in‐situ hybridization (FISH) and immunohistochemistry (IHC) constitute a pair of complimentary techniques for detecting gene amplification and overexpression, respectively. The advantages of IHC include relatively cheap materials and high sample durability, while FISH is the more accurate and reproducible method. Evaluation of FISH and IHC images is still largely performed manually, with automated or semiautomated techniques increasing in popularity. Here, we provide a comprehensive review of a number of (semi‐) automated FISH and IHC image processing systems, focusing on the algorithmic aspects of each technique. Our review verifies the increasingly important role of such methods in FISH and IHC; however, manual intervention is still necessary in order to resolve particularly challenging or ambiguous cases. In addition, large‐scale validation is required in order for these systems to enter standard clinical practice.

Assessment of Linear and Nonlinear Synchronization Measures for Analyzing EEG in a Mild Epileptic Paradigm

Epilepsy is one of the most common brain disorders and may result in brain dysfunction and cognitive disturbances. Epileptic seizures usually begin in childhood without being accommodated by brain damage and are tolerated by drugs that produce no brain dysfunction. In this study, cognitive function is evaluated in children with mild epileptic seizures controlled with common antiepileptic drugs. Under this prism, we propose a concise technical framework of combining and validating both linear and nonlinear methods to efficiently evaluate (in terms of synchronization) neurophysiological activity during a visual cognitive task consisting of fractal pattern observation. We investigate six measures of quantifying synchronous oscillatory activity based on different underlying assumptions. These measures include the coherence computed with the traditional formula and an alternative evaluation of it that relies on autoregressive models, an information theoretic measure known as minimum description length, a robust phase coupling measure known as phase-locking value, a reliable way of assessing generalized synchronization in state-space and an unbiased alternative called synchronization likelihood. Assessment is performed in three stages; initially, the nonlinear methods are validated on coupled nonlinear oscillators under increasing noise interference; second, surrogate data testing is performed to assess the possible nonlinear channel interdependencies of the acquired EEGs by comparing the synchronization indexes under the null hypothesis of stationary, linear dynamics; and finally, synchronization on the actual data is measured. The results on the actual data suggest that there is a significant difference between normal controls and epileptics, mostly apparent in occipital-parietal lobes during fractal observation tests.

Outcome prediction based on microarray analysis: a critical perspective on methods.

BackgroundInformation extraction from microarrays has not yet been widely used in diagnostic or prognostic decision-support systems, due to the diversity of results produced by the available techniques, their instability on different data sets and the inability to relate statistical significance with biological relevance. Thus, there is an urgent need to address the statistical framework of microarray analysis and identify its drawbacks and limitations, which will enable us to thoroughly compare methodologies under the same experimental set-up and associate results with confidence intervals meaningful to clinicians. In this study we consider gene-selection algorithms with the aim to reveal inefficiencies in performance evaluation and address aspects that can reduce uncertainty in algorithmic validation.ResultsA computational study is performed related to the performance of several gene selection methodologies on publicly available microarray data. Three basic types of experimental scenarios are evaluated, i.e. the independent test-set and the 10-fold cross-validation (CV) using maximum and average performance measures. Feature selection methods behave differently under different validation strategies. The performance results from CV do not mach well those from the independent test-set, except for the support vector machines (SVM) and the least squares SVM methods. However, these wrapper methods achieve variable (often low) performance, whereas the hybrid methods attain consistently higher accuracies. The use of an independent test-set within CV is important for the evaluation of the predictive power of algorithms. The optimal size of the selected gene-set also appears to be dependent on the evaluation scheme. The consistency of selected genes over variation of the training-set is another aspect important in reducing uncertainty in the evaluation of the derived gene signature. In all cases the presence of outlier samples can seriously affect algorithmic performance.ConclusionMultiple parameters can influence the selection of a gene-signature and its predictive power, thus possible biases in validation methods must always be accounted for. This paper illustrates that independent test-set evaluation reduces the bias of CV, and case-specific measures reveal stability characteristics of the gene-signature over changes of the training set. Moreover, frequency measures on gene selection address the algorithmic consistency in selecting the same gene signature under different training conditions. These issues contribute to the development of an objective evaluation framework and aid the derivation of statistically consistent gene signatures that could eventually be correlated with biological relevance. The benefits of the proposed framework are supported by the evaluation results and methodological comparisons performed for several gene-selection algorithms on three publicly available datasets.

Time-frequency analysis methods to quantify the time-varying microstructure of sleep EEG spindles: Possibility for dementia biomarkers?

The time-varying microstructure of sleep EEG spindles may have clinical significance in dementia studies and can be quantified with a number of techniques. In this paper, real and simulated sleep spindles were regarded as AM/FM signals modeled by six parameters that define the instantaneous envelope (IE) and instantaneous frequency (IF) waveforms for a sleep spindle. These parameters were estimated using four different methods, namely the Hilbert transform (HT), complex demodulation (CD), matching pursuit (MP) and wavelet transform (WT). The average error in estimating these parameters was lowest for HT, higher but still less than 10% for CD and MP, and highest (greater than 10%) for WT. The signal distortion induced by the use of a given method was greatest in the case of HT and MP. These two techniques would necessitate the removal of about 0.4s from the spindle data, which is an important limitation for the case of spindles with duration less than 1s. Although the CD method may lead to a higher error than HT and MP, it requires a removal of only about 0.23s of data. An application of this sleep spindle parameterization via the CD method is proposed, in search of efficient EEG-based biomarkers in dementia. Preliminary results indicate that the proposed parameterization may be promising, since it can quantify specific differences in IE and IF characteristics between sleep spindles from dementia subjects and those from aged controls.

Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury

Cross-frequency coupling (CFC) is thought to represent a basic mechanism of functional integration of neural networks across distant brain regions. In this study, we analyzed CFC profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 30 mild traumatic brain injury (mTBI) patients and 50 controls. We used mutual information (MI) to quantify the phase-to-amplitude coupling (PAC) of activity among the recording sensors in six nonoverlapping frequency bands. After forming the CFC-based functional connectivity graphs, we employed a tensor representation and tensor subspace analysis to identify the optimal set of features for subject classification as mTBI or control. Our results showed that controls formed a dense network of stronger local and global connections indicating higher functional integration compared to mTBI patients. Furthermore, mTBI patients could be separated from controls with more than 90% classification accuracy. These findings indicate that analysis of brain networks computed from resting-state MEG with PAC and tensorial representation of connectivity profiles may provide a valuable biomarker for the diagnosis of mTBI.

Intertrial coherence and causal interaction among independent EEG components

Over the past few years there has been an increased interest in studying the underlying neural mechanism of attention and cognitive brain activity. This paper aims towards identifying and analyzing distinct responses in an auditory working memory paradigm, as independent components with variable latency, frequency and phase characteristics. The event-related nature of components (either phase or non-phase-locked) over multiple trials is thoroughly examined through intertrial coherence measures. Furthermore, the functional coupling of independent components is investigated through the concept of partial directed coherence depicted as a directed graph. Using these tools, the paper compares issues of activation, connectivity and directionality in the synchronization maps of two populations, of control and Alzheimers subjects. The results on real data from an oddball experiment verify and further enhance the findings of previous studies and illustrate the potential of the proposed analysis framework.

Nonlinear enhancement and segmentation algorithm for the detection of age-related macular degeneration (AMD) in human eye's retina

Assessment of the risk for the development of age related macular degeneration requires reliable detection of retinal abnormalities that are considered as precursors of the disease. A typical sign for the latter are the so-called drusen, which appear as abnormal white-yellow deposits on the retina. This paper presents a novel segmentation algorithm for automatic detection of abnormalities in images of the human eyes retina, acquired from a depth-vision camera. Conventional image processing techniques are sensitive to non-uniform illumination and nonhomogeneous background, which obstructs the derivation of reliable results for a large set of different images. Homomorphic filtering and a multilevel variant of histogram equalization are used for non-uniform illumination compensation and enhancement. We develop a novel segmentation technique, the histogram-teased adaptive local thresholding (HALT), to detect drusen in retina images by extracting the useful information without being affected by the presence of other structures. We provide experimental results from the application of our technique to real images, where certain abnormalities (drusen) have slightly different characteristics from the background and are hard to be segmented by other conventional techniques.