Phase synchrony and coherence analyses of EEG as tools to discriminate between children with and without attention deficit disorder

Tcheslavski, Gleb V.; Beex, A.A.

doi:10.1016/j.bspc.2006.08.001

Cited by 23 publications

(14 citation statements)

References 5 publications

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“…This is just a little better than a pure guess. This performance is very similar to the result found based on phase synchrony processing of eyes closed EEG [23], an entirely different approach than GMM-UBM. As the performance of a KNN approach [4] was also good when using ANT data, improved detection performance appears to be highly correlated with using an attention task instead of the eyes closed condition.…”

Section: Methodssupporting

confidence: 85%

Classification of ADHD and non-ADHD subjects using a universal background model

Marcano

Bell

Beex

2018

Biomedical Signal Processing and Control

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ADHD affects a major portion of our children, predominantly boys. Upon diagnosis treatment can be offered that is usually quite effective. Diagnosis is generally based on subjective observation and interview. As a result, an objective test for the detection or presence of ADHD is considered very desirable. Based on EEG, across multiple channels, using autoregressive model parameters as features, ADHD detection is approached here in analogy with the imposter problem known from speaker verification. Gaussian mixture models are used to define ADHD and universal background models so that a likelihood ratio detector can be designed. The efficacy of this approach is reflected in the traditional detector performance measures of the area-under-the-curve and equal-error-probability. The results – based on a limited database of males, approximately 6 years of age – indicate that high probability of detection and low equal error rate can be achieved simultaneously with the proposed approach, when using EEG collected during an attention network task. The effect of using contaminated data is investigated as well.

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Section: Methodssupporting

confidence: 85%

Classification of ADHD and non-ADHD subjects using a universal background model

Marcano

Bell

Beex

2018

Biomedical Signal Processing and Control

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“…9,10 Similarly, EEG phase synchronization and coherence have been used to diagnose attention deficit disorder in children. 11 In behavioral studies, synchronization measurement has been useful in assessing coordination between individuals during interpersonal interaction (e.g., conversation, dance); [12][13][14] for tutorials, see Refs. 15 and 16.…”

Section: Introductionmentioning

confidence: 99%

Quantifying time-varying coordination of multimodal speech signals using correlation map analysis

Barbosa

Déchaine

Vatikiotis‐Bateson

et al. 2012

The Journal of the Acoustical Society of America

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This paper demonstrates an algorithm for computing the instantaneous correlation coefficient between two signals. The algorithm is the computational engine for analyzing the time-varying coordination between signals, which is called correlation map analysis (CMA). Correlation is computed around any pair of points in the two input signals. Thus, coordination can be assessed across a continuous range of temporal offsets and be detected even when changing over time due to temporal fluctuations. The correlation algorithm has two major features: (i) it is structurally similar to a tunable filter, requiring only one parameter to set its cutoff frequency (and sensitivity), (ii) it can be applied either uni-directionally (computing correlation based only on previous samples) or bi-directionally (computing correlation based on both previous and future samples). Computing instantaneous correlation for a range of time offsets between two signals produces a 2D correlation map, in which correlation is characterized as a function of time and temporal offset. Graphic visualization of the correlation map provides rapid assessment of how correspondence patterns progress through time. The utility of the algorithm and of CMA are exemplified using the spatial and temporal coordination of various audible and visible components associated with linguistic performance.

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“…Furthermore, CTS analysis provides a measure of phase coherence that can be applied to quantify EEG phase coherence changes due to experimentally controlled stimuli in clinically relevant frequencies that may be applicable to differentiating between patients with and without neurological disorders (Farmer, 2002; Tcheslavski & Beex, 2006; Doesburg, 2009). In contrast to traditional wavelet analysis, CTS can be applied to sparse data, similar to discrete Fourier transform (DFT), as long as the data satisfy the restrictions of the Nyquist theorem (Nyquist, 1928).…”

Section: Discussionmentioning

confidence: 99%

Delay Differential Analysis of Electroencephalographic Data

et al. 2015

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We propose a time-domain approach to detect frequencies, frequency couplings, and phases using nonlinear correlation functions. For frequency analysis, this approach is a multivariate extension of discrete Fourier transform, and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short and sparse time series and can be extended to cross-trial and cross-channel spectra (CTS) for electroencephalography data where multiple short data segments from multiple trials of the same experiment are available. There are two versions of CTS. The first one assumes some phase coherency across the trials, while the second one is independent of phase coherency. We demonstrate that the phase-dependent version is more consistent with event-related spectral perturbation analysis and traditional Morlet wavelet analysis. We show that CTS can be applied to short data windows and yields higher temporal resolution than traditional Morlet wavelet analysis. Furthermore, the CTS can be used to reconstruct the event-related potential using all linear components of the CTS.

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Phase synchrony and coherence analyses of EEG as tools to discriminate between children with and without attention deficit disorder

Cited by 23 publications

References 5 publications

Classification of ADHD and non-ADHD subjects using a universal background model

Classification of ADHD and non-ADHD subjects using a universal background model

Quantifying time-varying coordination of multimodal speech signals using correlation map analysis

Delay Differential Analysis of Electroencephalographic Data

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