Canonical bicoherence analysis of dynamic EEG data

He, Huixia; Thomson, David J.

doi:10.1007/s10827-009-0177-z

Cited by 11 publications

(5 citation statements)

References 66 publications

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“…highorder spectra methods, 11,[30][31][32][33][34] are often used to study the nervous system in both animals 35,36 and humans. [37][38][39][40][41] Bicoherence has been proposed to quantify the second-order phase coupling (also called quadratic phase coupling, QPC) among three frequencies, i.e.…”

Section: Introductionmentioning

confidence: 99%

A General Approach for Quantifying Nonlinear Connectivity in the Nervous System Based on Phase Coupling

Yang

Solis‐Escalante

Yao

et al. 2016

Int. J. Neur. Syst.

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Interaction between distant neuronal populations is essential for communication within the nervous system and can occur as a highly nonlinear process. To better understand the functional role of neural interactions, it is important to quantify the nonlinear connectivity in the nervous system. We introduce a general approach to measure nonlinear connectivity through phase coupling: the multi-spectral phase coherence (MSPC). Using simulated data, we compare MSPC with existing phase coupling measures, namely n : m synchronization index and bi-phase locking value. MSPC provides a system description, including (i) the order of the nonlinearity, (ii) the direction of interaction, (iii) the time delay in the system, and both (iv) harmonic and (v) intermodulation coupling beyond the second order; which are only partly revealed by other methods. We apply MSPC to analyze data from a motor control experiment, where subjects performed isotonic wrist flexions while receiving movement perturbations. MSPC between the perturbation, EEG and EMG was calculated. Our results reveal directional nonlinear connectivity in the afferent and efferent pathways, as well as the time delay (43 ± 8 ms) between the perturbation and the brain response. In conclusion, MSPC is a novel approach capable to assess high-order nonlinear interaction and timing in the nervous system.

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Section: Introductionmentioning

confidence: 99%

A General Approach for Quantifying Nonlinear Connectivity in the Nervous System Based on Phase Coupling

Yang

Solis‐Escalante

Yao

et al. 2016

Int. J. Neur. Syst.

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“…However, unlike DCM-IR, these are limited to within-frequency relationships. Methods evaluating both within and cross-frequency relationships are rarer, including bicoherence, n:m phase synchrony, and multi-spectral phase coherence methods [75][76][77][78] . However, these methods either do not evaluate directionality, like correlations and coherence, or do not directly link to cortical regions.…”

Section: Limitationsmentioning

confidence: 99%

Intervention-Induced Changes in Cortical Connectivity and Activity in Severe Chronic Hemiparetic Stroke

Wilkins

Yao

2019

Preprint

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Effective interventions have demonstrated the ability to improve motor function by reengaging ipsilesional resources, which has been shown to be critical and feasible for hand function recovery even in individuals with severe chronic stroke. However, previous studies focus on changes in brain activity related to motor execution. How changes in motor preparation may facilitate these changes at motor execution is still unclear. In order to address this question, we had 8 individuals with severe chronic hemiparetic stroke participate in a device-assisted intervention. We then quantified changes in both coupling between regions during motor preparation and changes in topographical cortical activity at motor execution for both the hand in isolation and together with the shoulder using high density EEG. We hypothesized that intervention-induced changes in cortico-cortico interactions during motor preparation would lead to changes in activity at motor execution specifically towards an increased reliance on the ipsilesional hemisphere. We found that, following the intervention, individuals displayed a reduction in coupling from ipsilesional M1 to contralesional M1 within gamma frequencies during motor preparation for hand opening. This was followed by a reduction in activity in the contralesional primary sensorimotor cortex during motor execution. Meanwhile, during lifting and opening, a shift to inhibitory coupling within ipsilesional M1 from gamma to beta frequencies was accompanied by an increase in ipsilesional primary sensorimotor cortex activity following the intervention. Together, these results show that intervention-induced changes in coupling within or between motor regions during motor preparation lead to corresponding changes in focal cortical activity at execution.

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“…Previous studies of neural activity revealed functional connectivity via QPC in time series of electroencephalography [19] [20] [21] [22] [23] [24], local field potentials [25] [26] [27], and magnetoencephalography [27] [28]. Detection of QPC in non-stationary signals of electroencephalography has also been presented with a time-varying extension of the definition of bicoherence [29]. Beyond the application to brain signals, it is worth mentioning the application of bispectrum to reveal phase coupling in renal blood flow [30] and the detection of QPC in geomagnetic data with a modified definition of bicoherence [31] [32].…”

Section: Introductionmentioning

confidence: 99%

Detection of phase coupling in interacting networks of cortical columns

Abé¹,

Asai²,

Villa³

2022

Preprint

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<div>This is a preprint version submitted to review for publication on the IEEE Transactions on Biomedical Engineering (TBME).</div><div><br></div><div>In this study we investigated the estimation method of cross-bicoherence to infer quadratic phase coupling (QPC) in interaction networks based on empirical Bayes estimation. Nonlinear interaction, ubiquitous in a wide range of biomedical fields, often makes it challenging to apply conventional parametric approaches. While cross-bicoherence has been recognized as a key statistic of QPC, prior studies have not elucidated how reliable the estimation can be for realistic time series. This work demonstrates that cross-bicoherence estimated by the proposed method effectively detects QPC, with illustrative simulation studies of both typical QPC and neural mass models of cortical columns.<br></div>

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Canonical bicoherence analysis of dynamic EEG data

Cited by 11 publications

References 66 publications

A General Approach for Quantifying Nonlinear Connectivity in the Nervous System Based on Phase Coupling

A General Approach for Quantifying Nonlinear Connectivity in the Nervous System Based on Phase Coupling

Intervention-Induced Changes in Cortical Connectivity and Activity in Severe Chronic Hemiparetic Stroke

Detection of phase coupling in interacting networks of cortical columns

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