Parametric and Nonparametric EEG Analysis for the Evaluation of EEG Activity in Young Children with Controlled Epilepsy

There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform) and a parametric, signal modeling technique (ARMA) are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest) task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier

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

This work was supported in part by the EC-IST project Biopattern, contract no: 508803, by the EC ICT project TUMOR, contract no: 247754, by the University of Malta grant LBA-73-695, by an internal grant from the Technical University of Crete, ELKE# 80037 and by the Academy of Finland, project nos: 113572, 118355, 134767 and 213462.Background: In 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. Methods: We 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. Results: Differences 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. Conclusions: Based 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.peer-reviewe

Does Greater Low Frequency EEG Activity in Normal Immaturity and in Children with Epilepsy Arise in the Same Neuronal Network?

Greater low frequency power (<8Hz) in the electroencephalogram (EEG) at rest is normal in the immature developing brain of children when compared to adults. Children with epilepsy also have greater low frequency interictal resting EEG activity. Whether these power elevations reflect brain immaturity due to a developmental lag or the underlying epileptic pathophysiology is unclear. The present study addresses this question by analyzing spectral EEG topographies and sources for normally developing children and children with epilepsy. We first compared the resting EEG of healthy children to that of healthy adults to isolate effects related to normal brain immaturity. Next, we compared the EEG from 10 children with generalized cryptogenic epilepsy to the EEG of 24 healthy children to isolate effects related to epilepsy. Spectral analysis revealed that global low (delta: 1-3Hz, theta: 4-7Hz), medium (alpha: 8-12Hz) and high (beta: 13-25Hz) frequency EEG activity was greater in children without epilepsy compared to adults, and even further elevated for children with epilepsy. Topographical and tomographic EEG analyses showed that normal immaturity corresponded to greater delta and theta activity at fronto-central scalp and brain regions, respectively. In contrast, the epilepsy-related activity elevations were predominantly in the alpha band at parieto-occipital electrodes and brain regions, respectively. We conclude that lower frequency activity can be a sign of normal brain immaturity or brain pathology depending on the specific topography and frequency of the oscillating neuronal networ

Brain Wave Biofeedback: Benefits of Integrating Neurofeedback in Counseling

Consistent with the 2009 Standards of the Council for Accreditation of Counseling and Related Educational Programs, counselors must understand neurobiological behavior in individuals of all developmental levels. This requires understanding the brain and strategies for applying neurobiological concepts in counseling practice, training, and research. Neurofeedback, biofeedback for the brain, is one modality based in neuroscience that empowers individuals to recognize, monitor, and self-regulate brain wave activity to create greater wellness. Neurofeedback has significant potential in counseling preparation, research, and practice

Genetic analysis of human absence epilepsy

Idiopathic Mendelian epilepsies have been typically identified as channelopathies. Evidence suggests that mutations in genes encoding GABAA receptors, GABAB receptors or voltage-dependent calcium channels (VDCCs) may underlie childhood absence epilepsy (CAE), an idiopathic generalised epilepsy with complex inheritance. The aims of this project were: i) Ascertainment of a patient resource ii) Investigation of candidate genes by linkage analysis iii) Mutation analysis by direct sequencing iv) Construction of single nucleotide polymorphism (SNP) based haplotypes in candidate genes v) Intra-familial association analysis using SNP based haplotypes DNA and clinical data were obtained from: 53 nuclear CAE pedigrees; 29 families including individuals with CAE and a broader „absence‟ epilepsy phenotype; 217 parent-child trios; a North American family in which absence epilepsy segregates with episodic ataxia type 2 (EA2) Sixteen calcium channel genes and seven GABAA and two GABAB receptor subunit genes were excluded by linkage analysis. Significant linkage was demonstrated for CACNG3 on chromosome 16p12-p13.1 for both CAE and the broader absence phenotype. Positive linkage was also obtained at the GABRA5, GABRB3, GABRG3 cluster on chromosome 15q11-q13. Non-parametric linkage analysis was significant at both the 16p and 15q loci. Two-locus analysis supported a digenic effect from these two loci. Sequencing of CACNG3 revealed 34 sequence variants, none clearly causal, although bioinformatic analysis provided supportive functional evidence. Association analysis showed significant transmission disequilibrium both for individual single nucleotide polymorphisms (SNPs) and SNP based haplotypes spanning CACNG3. This work has provided genetic evidence that CACNG3 and at least one of the three GABAA receptor genes are susceptibility loci for absence epilepsy. Linkage analysis performed in the family with absence epilepsy and EA2 was suggestive that the VDCC CACNA1A was the causative gene. This was subsequently confirmed by sequence analysis in collaboration with the Institute of Neurology, UCL. This is the first reported family in which a CACNA1A mutation that impairs calcium channel function cosegregates with typical absence seizures and 3Hz spike-wave discharges on EEG

Differentiating Epileptic from Psychogenic Nonepileptic EEG Signals using Time Frequency and Information Theoretic Measures of Connectivity

Differentiating psychogenic nonepileptic seizures from epileptic seizures is a difficult task that requires timely recording of psychogenic events using video electroencephalography (EEG). Interpretation of video EEG to distinguish epileptic features from signal artifacts is error prone and can lead to misdiagnosis of psychogenic seizures as epileptic seizures resulting in undue stress and ineffective treatment with antiepileptic drugs. In this study, an automated surface EEG analysis was implemented to investigate differences between patients classified as having psychogenic or epileptic seizures. Surface EEG signals were grouped corresponding to the anatomical lobes of the brain (frontal, parietal, temporal, and occipital) and central coronal plane of the skull. To determine if differences were present between psychogenic and epileptic groups, magnitude squared coherence (MSC) and cross approximate entropy (C-ApEn) were used as measures of neural connectivity. MSC was computed within each neural frequency band (delta: 0.5Hz-4Hz, theta: 4-8Hz, alpha: 8-13Hz, beta: 13-30Hz, and gamma: 30-100Hz) between all brain regions. C-ApEn was computed bidirectionally between all brain regions. Independent samples t-tests were used to compare groups. The statistical analysis revealed significant differences between psychogenic and epileptic groups for both connectivity measures with the psychogenic group showing higher average connectivity. Average MSC was found to be lower for the epileptic group between the frontal/central, parietal/central, and temporal/occipital regions in the delta band and between the temporal/occipital regions in the theta band. Average C-ApEn was found to be greater for the epileptic group between the frontal/parietal, parietal/frontal, parietal/occipital, and parietal/central region pairs. These results suggest that differences in neural connectivity exist between psychogenic and epileptic patient groups

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks