coherence
Research Papers
Showing 6 of 8The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG
An ongoing interest towards identification based on biosignals, such as electroencephalogram (EEG), magnetic resonance imaging (MRI), is growing in the past decades. Previous studies indicated that the inherent information about brain activity may be used to identify individual during resting-state of eyes open (REO) and eyes closed (REC). Electroencephalographic (EEG) records the data from the scalp, and it is believed that the noisy EEG signals can influence the accuracies of one experiment causing unreliable results. Therefore, the stability and time-robustness of inter-individual features can be investigated for the purpose of individual identification. In this work, we conducted three experiments with the time interval of at least 2 weeks, and used different types of measures (Power Spectral Density, Cross Spectrum, Channel Coherence and Phase Lags) to extract the individual features. The Pearson Correlation Coefficient (PCC) is calculated to measure the level of linear correlation for intra-individual, and Support Vector Machine (SVM) is used to obtain the related classification accuracy. Results show that the classification accuracies of four features were 85-100% for intra-experiment dataset, and were 80-100% for fusion experiments dataset. For inter-experiments classification of REO features, the optimized frequency range is 13-40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. For inter-experiments classification of REC, the optimized frequency range is 8-40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. The classification results of Phase Lags are much lower than the other three features. These results show the time-robustness of EEG, which can further use for individual identification system.
View Full Paper →Connectivity-Guided EEG Biofeedback for Autism Spectrum Disorder: Evidence of Neurophysiological Changes
Recent studies have linked neural coherence deficits with impairments associated with Autism Spectrum Disorders (ASD). The current study tested the hypothesis that lowering neural hyperconnectivity would lead to decreases in autistic symptoms. Subjects underwent connectivity-guided EEG biofeedback, which has been previously found to enhance neuropsychological functioning and to lessen autistic symptoms. Significant reductions in neural coherence across frontotemporal regions and source localized power changes were evident in frontal, temporal, and limbic regions following this treatment. Concurrently, there were significant improvements on objective neuropsychological tests and parents reported positive gains (decreases in symptoms) following the treatment. These findings further validate EEG biofeedback as a therapeutic modality for autistic children and suggest that changes in coherence anomalies may be related to the mechanism of action.
View Full Paper →Validity and Reliability of Quantitative Electroencephalography
Reliability and validity are statistical concepts that are reviewed and then applied to the field of quantitative electroencephalography (qEEG). The review of the scientific literature demonstrated high levels of split-half and test–retest reliability of qEEG and convincing content and predictive validity as well as other forms of validity. QEEG is distinguished fromnonquantitative EEG (“eyeball” examination of EEG traces), with the latter showing low reliability (e.g., 0.2–0.29) and poor interrater agreement for nonepilepsy evaluation. In contrast, qEEG is greater than 0.9 reliable with as little as 40-s epochs and remains stable with high test–retest reliability over many days and weeks. Predictive validity of qEEG is established by significant and replicable correlations with clinical measures and accurate predictions of outcome and performance on neuropsychological tests. In contrast, non-qEEG or eyeball visual examination of the EEG traces in cases of nonepilepsy has essentially zero predictive validity. Content validity of qEEG is established by correlations with independent measures such as the MRI, PET and SPECT, the Glasgow Coma Score, neuropsychological tests, and so on, where the scientific literature again demonstrates significant correlations between qEEG and independent measures known to be related to various clinical disorders. The ability to test and evaluate the concepts of reliability and validity are demonstrated by mathematical proof and simulation where one can demonstrate test–retest reliability as well as zero physiological validity of coherence and phase differences when using an average reference and Laplacian montage.
View Full Paper →Functional Connectivity and Aging: Comodulation and Coherence Differences
Introduction. Misconceptions about coherence and comodulation has hindered their simultaneous use in assessing electroencephalography (EEG). Coherence refers to phase synchrony, whereas comodulation refers to magnitude synchrony. Child and adult EEG data were analyzed for age functions to demonstrate coherence and comodulation differences. Method. Eyes closed resting EEG was analyzed for 101 children and adults between ages of 5 and 35 years (34 female, 67 male; M age = 17.5 years). Spectral analysis focused on site-centered connectivity of 10 frequency bands. Site-centered connectivity refers to averaged coherence or comodulation associated with a site, an estimate of its network traffic. Results. Site-centered coherence and comodulation increased with age for frequencies below 30 Hz in most sites. Age-related changes in anterior connectivity occurred for adults but not for children. The strongest age function was found for alpha comodulation at electrode site T5. Differences in coherence and comodulation results are also reported. Conclusion. Functional connectivity increases steadily with age. Anterior EEG connectivity increased during adulthood but not during childhood. This finding parallels previous research on anterior callosal myelination and suggests that EEG connectivity measures may in part reflect myelination patterns. A model that associates coherence and comodulation with feedforward and feedback activity of the brain is proposed. A Periodicity Table for creating new and potentially relevant psychophysiological coefficients was described.
View Full Paper →Introduction to Advances in EEG Connectivity
This special issue of the Journal of Neurotherapy has been devoted to Advances in EEG Connectivities. These purposes include providing education to our readers and collaboration among the scientists and authors. Multiple connectivity metrics have been defined with an emphasis on coherence and multivariate connectivity measures. The goals of connectivity measurements should include accuracy compared to known neurological networks and utility in assessment and application for intervention (e.g., EEG coherence training). It is hoped that the information contained in this special issue will form the basis for future advancements in EEG connectivity assessment and intervention.
View Full Paper →Towards a Coherent View of Brain Connectivity
Background. The electroencephalogram provides a myriad of opportunities to detect and assess brain function and brain connectivity. Method. This article describes the relationship between local and non-local brain activation and synchrony, and discusses the use of appropriate connectivity measures to study and train functional brain connectivity. Specific connectivity measures are described including coherence, phase, synchrony, correlation, and comodulation. The measures are contrasted and compared in terms of their ability to detect particular aspects of connectivity and their usefulness for neurofeedback training. Results. Connectivity metrics for example EEG data are calculated and shown graphically, to illustrate relevant principles. Conclusion. It is possible to assess brain connectivity and integrated function for both assessment and training, through the use of appropriate metrics and display methods.
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