Kostas Michalopoulos

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.

The independent components of auditory P300 and CNV evoked potentials derived from single-trial recordings

The back-projected independent components (BICs) of single-trial, auditory P300 and contingent negative variation (CNV) evoked potentials (EPs) were derived using independent component analysis (ICA) and cluster analysis. The method was tested in simulation including a study of the electric dipole equivalents of the signal sources. P300 data were obtained from healthy and Alzheimers disease (AD) subjects. The BICs were of approximately 100 ms duration and approximated positive- and negative-going half-sinusoids. Some positively and negatively peaking BICs constituting the P300 coincided with known peaks in the averaged P300. However, there were trial-to-trial differences in their occurrences, particularly where a positive or a negative BIC could occur with the same latency in different trials, a fact which would be obscured by averaging them. These variations resulted in marked differences in the shapes of the reconstructed, artefact-free, single-trial P300s. The latencies of the BIC associated with the P3b peak differed between healthy and AD subjects (p < 0.01). More reliable evidence than that obtainable from single-trial or averaged P300s is likely to be found by studying the properties of the BICs over a number of trials. For the CNV, BICs corresponding to both the orienting and the expectancy components were found.

Assessment of neural dynamic coupling and causal interactions between independent EEG components from cognitive tasks using linear and nonlinear methods

Over the past few years there has been an increased interest in studying the underlying neural mechanism of cognitive brain activity. In this direction, we study the brain activity based on its independent components instead of the EEG signal itself. Both linear and nonlinear synchronization measures are applied to EEG components, which are free of volume conduction effects and background noise. More specifically, a robust nonlinear state-space generalized synchronization assessment method and the recently introduced partial directed coherence are investigated in a working memory paradigm, during mental rehearsal of pictures. The latter is a linear method able to assess not only the independence of the brain regions, but also the direction of the statistically significant relationships. The results are in accordance with previous psychophysiology studies suggesting increased synchrony between prefrontal and parietal components during the rehearsal process, most prominently in gamma (ca. 40 Hz) band. This study indicates that functional connectivity during cognitive processes may be successfully assessed using independent components, which reflect distinct spatial patterns of activity.

Combining EEG Microstates with fMRI Structural Features for Modeling Brain Activity

Combining information from Electroencephalography (EEG) and Functional Magnetic Resonance Imaging (fMRI) has been a topic of increased interest recently. The main advantage of the EEG is its high temporal resolution, in the scale of milliseconds, while the main advantage of fMRI is the detection of functional activity with good spatial resolution. The advantages of each modality seem to complement each other, providing better insight in the neuronal activity of the brain. The main goal of combining information from both modalities is to increase the spatial and the temporal localization of the underlying neuronal activity captured by each modality. This paper presents a novel technique based on the combination of these two modalities (EEG, fMRI) that allow a better representation and understanding of brain activities in time. EEG is modeled as a sequence of topographies, based on the notion of microstates. Hidden Markov Models (HMMs) were used to model the temporal evolution of the topography of the average Event Related Potential (ERP). For each model the Fisher score of the sequence is calculated by taking the gradient of the trained model parameters. The Fisher score describes how this sequence deviates from the learned HMM. Canonical Partial Least Squares (CPLS) were used to decompose the two datasets and fuse the EEG and fMRI features. In order to test the effectiveness of this method, the results of this methodology were compared with the results of CPLS using the average ERP signal of a single channel. The presented methodology was able to derive components that co-vary between EEG and fMRI and present significant differences between the two tasks.

A decision support framework for the discrimination of children with controlled epilepsy based on EEG analysis

BackgroundIn this work we consider hidden signs (biomarkers) in ongoing EEG activity expressing epileptic tendency, for otherwise normal brain operation. More specifically, this study considers children with controlled epilepsy where only a few seizures without complications were noted before starting medication and who showed no clinical or electrophysiological signs of brain dysfunction. We compare EEG recordings from controlled epileptic children with age-matched control children under two different operations, an eyes closed rest condition and a mathematical task. The aim of this study is to develop reliable techniques for the extraction of biomarkers from EEG that indicate the presence of minor neurophysiological signs in cases where no clinical or significant EEG abnormalities are observed.MethodsWe compare two different approaches for localizing activity differences and retrieving relevant information for classifying the two groups. The first approach focuses on power spectrum analysis whereas the second approach analyzes the functional coupling of cortical assemblies using linear synchronization techniques.ResultsDifferences could be detected during the control (rest) task, but not on the more demanding mathematical task. The spectral markers provide better diagnostic ability than their synchronization counterparts, even though a combination (or fusion) of both is needed for efficient classification of subjects.ConclusionsBased on these differences, the study proposes concrete biomarkers that can be used in a decision support system for clinical validation. Fusion of selected biomarkers in the Theta and Alpha bands resulted in an increase of the classification score up to 80% during the rest condition. No significant discrimination was achieved during the performance of a mathematical subtraction task.

Classification of EEG Single Trial Microstates Using Local Global Graphs and Discrete Hidden Markov Models

We present a novel synergistic methodology for the spatio-temporal analysis of single Electroencephalogram (EEG) trials. This new methodology is based on the novel synergy of Local Global Graph (LG graph) to characterize define the structural features of the EEG topography as a global descriptor for robust comparison of dominant topographies (microstates) and Hidden Markov Models (HMM) to model the topographic sequence in a unique way. In particular, the LG graph descriptor defines similarity and distance measures that can be successfully used for the difficult comparison of the extracted LG graphs in the presence of noise. In addition, hidden states represent periods of stationary distribution of topographies that constitute the equivalent of the microstates in the model. The transitions between the different microstates and the formed syntactic patterns can reveal differences in the processing of the input stimulus between different pathologies. We train the HMM model to learn the transitions between the different microstates and express the syntactic patterns that appear in the single trials in a compact and efficient way. We applied this methodology in single trials consisting of normal subjects and patients with Progressive Mild Cognitive Impairment (PMCI) to discriminate these two groups. The classification results show that this approach is capable to efficiently discriminate between control and Progressive MCI single trials. Results indicate that HMMs provide physiologically meaningful results that can be used in the syntactic analysis of Event Related Potentials.

Waveform analysis of non-oscillatory independent components in single-trial auditory event-related activity in healthy subjects and Alzheimer's disease patients.

The objective was to characterize the non-oscillatory independent components (ICs) of the auditory event-related potential (ERP) waveform of an oddball task for normal and newly diagnosed Alzheimers disease (AD) subjects, and to seek biomarkers for AD. Single trial ERP waveforms were analysed using independent components analysis (ICA) and k-means clustering. Two stages of clustering depended upon the magnitudes and latencies, and the scalp topographies of the non-oscillatory back-projected ICs (BICs) at electrode Cz. The electrical current dipole sources of the BICs were located using Low Resolution Electromagnetic Tomography (LORETA). Generally 3-10 BICs, of different latencies and polarities, occurred in each trial. Each peak was associated with positive and negative BICs. The trial-to-trial variations in their relative numbers and magnitudes may explain the variations in the averaged ERP reported, and the delay in the averaged P300 for AD patients. The BIC latencies, topographies and electrical current density maximum locations varied from trial-to-trial. Voltage foci in the BIC topographies identify the BIC source locations. Since statistical differences were found between the BICs in healthy and AD subjects, the method might provide reliable biomarkers for AD, if these findings are reproduced in a larger study, independently of other factors influencing the comparison of the two populations. The method can extract artefact- and EEG-free single trial ERP waveforms, offers improved ERP averages by selecting the trials on the basis of their BICs, and is applicable to other evoked potentials, conditions and diseases.

Microstate analysis of the EEG using local global graphs

The topography of the electrical field does not vary randomly with time but rather displays short periods of stable topographical configurations or spatial patterns, known as microstates. The search of such patterns takes place in the high dimensional electrode space with all the problems that comes with it. In this paper, we present a technique for the extraction, detection and representation of EEG microstates based on the Local Global graph(LG graph). We use the Local Global graph to represent the spatial configuration of the topographic map and use a LG graph matching approach to determine the different microstates. The proposed method was applied on the average, over trials, Event related potential recording presenting a positive peak known as P300. Five dominant microstates were identified using our methodology. During the whole period of the P300 peak there is one active microstate which presents frontal and parietal topography which is expected for the phase locked activations of the P300. The LG graph modeling of the EEG topography provides a flexible descriptor for the EEG topography and can be used for the efficient microstate segmentation of the EEG.

Activity detection and causal interaction analysis among independent EEG components from memory related tasks

Over the past few years there has been an increased interest in studying the underlying neural mechanism of cognitive brain activity related to memory. In this direction, we study the brain activity based on its independent components instead of the EEG signal itself aiming towards identifying and analyzing induced responses being attributed to oscillatory bursts from local or distant neural assemblies, with variable latency and frequency, in an auditory working memory paradigm. The contribution and functional coupling of independent components to evoked and/or induced oscillatory activities is investigated through the concept of the recently introduced partial directed coherence method, which can also reveal the direction of the statistically significant relationships. The results on read data from an oddball experiment are in accordance with previous psychophysiology studies suggesting increased phase locked activity most prominently in the delta/ theta band, while alpha is also apparent in measures of non phase-locked activity. Dynamic synchronization is inferred between the alpha and delta bands, whereas some influence of the theta band is also detected. This study indicates that functional connectivity during cognitive processes may be successfully assessed using spectral power measures applied on independent components, which reflect distinct spatial patterns of activity.

Application of Decomposition Methods in the Filtering of Event-Related Potentials

The processes giving rise to an event-related potential engage several evoked and induced oscillatory components, which reflect phase or nonphase locked activity throughout the multiple trials of an experiment. The separation and identification of such components could not only serve diagnostic purposes but also facilitate the design of brain–computer interface systems. However, the effective analysis of components is hindered by many factors including the complexity of the EEG signal and its variation over the trials. In this chapter, we study several measures for the identification of the nature of independent components and propose a complete methodology for efficient decomposition of the rich information content embedded in the multichannel EEG recordings associated with the multiple trials of an event-related experiment. The efficiency of the proposed methodology is demonstrated through simulated and real experiments.