Assessment of changing interdependencies between human electroencephalograms using nonlinear methods

Pereda, Ernesto; Rial, Rubén V.; Gamundı́, Antoni; González, José M.

doi:10.1016/s0167-2789(00)00190-1

Cited by 76 publications

(47 citation statements)

References 29 publications

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“…3, 4) in the state spaces. This index has been found to be usef ul for simulated models (Schmitz, 2000) and also for real life signals (Pereda et al, 2001).…”

Section: Methodsmentioning

confidence: 95%

Long-Range Synchrony in the γ Band: Role in Music Perception

2001

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Synchronization seems to be a central mechanism for neuronal information processing within and between multiple brain areas. Furthermore, synchronization in the ␥ band has been shown to play an important role in higher cognitive functions, especially by binding the necessary spatial and temporal information in different cortical areas to build a coherent perception. Specific task-induced (evoked) ␥ oscillations have often been taken as an indication of synchrony, but the presence of longrange synchrony cannot be inferred from spectral power in the ␥ range. We studied the usefulness of a relatively new measure, called similarity index to detect asymmetric interdependency between two brain regions. Spontaneous EEG from two groups-musicians and non-musicians-were recorded during several states: listening to music, listening to text, and at rest (eyes closed and eyes open). While listening to music, degrees of the ␥ band synchrony over distributed cortical areas were found to be significantly higher in musicians than nonmusicians. Yet no differences between these two groups were found at resting conditions and while listening to a neutral text. In contrast to the degree of long-range synchrony, spectral power in the ␥ band was higher in non-musicians. The degree of spatial synchrony, a measure of signal complexity based on eigen-decomposition method, was also significantly increased in musicians while listening to music. As compared with nonmusicians, the finding of increased long-range synchrony in musicians independent of spectral power is interpreted as a manifestation of a more advanced musical memory of musicians in binding together several features of the intrinsic complexity of music in a dynamical way. Key words: EEG; synchronization; music; ␥ band; cognitive task; binding; similarity indexWidespread oscillatory activity, particularly in the ␥ range (Ͼ30 Hz) has attracted the attention of researchers studying different cognitive phenomena in human and mammalian species (for review, see Basar-Eroglu et al., 1996;Tallon-Baudry and Bertrand, 1999). Synchronous 40 Hz oscillations, found in the olfactory system of the rabbit (Freeman, 1978), were supposed to play a key role in the detection of different odors (Freeman and Skarda, 1985). Furthermore, spatially distributed cells in the visual cortex of both the anesthetized (Gray et al., 1989) and the alert cat (Gray and DiPrisco, 1997) produced oscillations in the ␥ band in response to visual stimuli. Evidence of precise phase locking across different cortical areas with zero phase lag in this frequency range was also reported in alert cats during visual discrimination task (Roelfsema et al., 1997). In humans, transient phase locking at ϳ40 Hz generated in the contralateral and parietal cortical areas was found during selective attention (Desmedt and Tomberg, 1994). It has been postulated (Bressler et al., 1993;Tallon-Baudry et al., 1998) that the ␥ band serves as a mechanism for the visual representation of objects and as a means of "binding" various intricate as...

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“…3, 4) in the state spaces. This index has been found to be usef ul for simulated models (Schmitz, 2000) and also for real life signals (Pereda et al, 2001).…”

Section: Methodsmentioning

confidence: 95%

Long-Range Synchrony in the γ Band: Role in Music Perception

2001

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“…These parameters should be determined properly to avoid loss of information in the reconstructed state-space. It is suggested that to choose the same values of T and d for all signals to be able to compare the similarity of their states (Pereda et al 2001). According to Taken's theorem (Chan and Tong 2013), the state vector of the i th sample of a given signal is defined as:…”

Section: State-space Trajectorymentioning

confidence: 99%

Assessment of visibility graph similarity as a synchronization measure for chaotic, noisy and stochastic time series

Ahmadi

Besseling

Pechenizkiy

2018

Soc. Netw. Anal. Min.

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Finding synchronization between the outputs of a dynamic system, which are represented mostly as time series, helps to characterize the system activities during an occurrence. An important issue in analyzing time series is that they may behave chaotically or stochastically. Therefore, applying a reliable synchronization measure which can capture the dynamic features of the system helps to quantify the interdependencies between time series, correctly. In this paper, we employ similarity measures based on visibility graph (VG) algorithms as an alternative and radically different way to measure the synchronization between time series. We assess the performance of VG-based similarity measures on chaotic, noisy and stochastic time series. In our experiments, we use the Rössler system and the noisy Hénon map as representative instances of chaotic systems, and the Kuramoto model for studying detection of synchronization between stochastic time series. Our study suggests that the similarity measure based on the horizontal VG algorithm should be favored to other measures for detecting synchronization between chaotic and stochastic time series.

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“…A number of studies have used this concept to find the relationship or interdependence among time series (Pereda et al 2001, Chicharro and Andrzejak 2009, Arnhold et al 1999). Bauer et al (2007b) has used nearest neighbours method for for the diagnosis of plant-wide disturbances.…”

Section: Causality Analysismentioning

confidence: 99%

“…Interdependence between two time series x(t) and y(t) using the "nearest neighbors" (NN) from their respective attractor manifolds has been used in number of studies for example Pereda et al (2001), Chicharro and Andrzejak (2009), and Arnhold et al (1999). The similar approach has been adopted by Bauer et al (2007b) for diagnosis of plant-wide disturbances.…”

Section: Introductionmentioning

confidence: 99%

A Delay Vector Variance based Approach for Detecting and Isolating the Non-linearity Induced Oscillations in Control Loops

Aftab¹,

Hovd²,

Sivalingam

2017

IFAC-PapersOnLine

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SummaryThe profitability of any industrial process is closely related to its ability to maintain near optimal operating conditions; therefore robust methods for root cause detection of any control performance degradation is crucial not only to maintain the desired operation of any industrial process but can result in marked improvement in productivity and over all economics.In a large and complex industrial control system, disturbances/oscillations originating at one point tend to propagate both up-and downstream due to underlying interactions, process flows and recycle streams, thus leading to plant-wide effects. Effective controller performance monitoring therefore requires detection and diagnosis of such plant-wide disturbances so that targeted maintenance can be achieved within the shortest possible time.Oscillations are considered to be the most important indicator of control performance degradation. Oscillations not only cause product variability and effect quality but also lead to adverse economical consequences owing to loss of precious resources like raw material and energy. Therefore, automated detection and diagnosis of oscillations needs to be reliable and effective as the large scale of the control system in industrial process plants makes manual observation and diagnosis practically impossible.Detection and diagnosis of oscillatory control loops is not trivial due to the causes such as presence of multiple oscillations, unknown process dynamics, non stationary effects and noise corruption, to name a few. The work presented in this thesis is aimed at addressing these challenges and at the development of improved tools for both detection and diagnosis of oscillations and plant-wide disturbances. Moreover, data driven methods are preferred over model based diagnostics as they are more general and are independent of a specific plant model. Dynamic proiii iv Summary cess models can be costly to develop and maintain, and for many process plants dynamic models are not available.This thesis has focussed on using the multivariate empirical mode decomposition (MEMD) and associated characteristics for the detection and diagnosis of oscillations in control loops. The methods proposed in thesis are aimed at addressing the shortcomings of the existing approaches and are fully data driven that require no a priori knowledge about the data or process itself. An improved approach for the oscillation detection has been presented that caters for non-stationary effects and reduces the mode mixing problems associated with the univariate empirical mode decomposition. Moreover, MEMD along with the proposed grouping algorithm provides a robust method to detect the plant-wide oscillations where different control variables, having common signatures of oscillations, are oscillating due to the same root cause. This helps in searching for the root cause only in the affected variables.Another important contribution of the thesis is an automated detection of harmonics in an oscillating signal. Non-linearity induced oscillations g...

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Assessment of changing interdependencies between human electroencephalograms using nonlinear methods

Cited by 76 publications

References 29 publications

Long-Range Synchrony in the γ Band: Role in Music Perception

Long-Range Synchrony in the γ Band: Role in Music Perception

Assessment of visibility graph similarity as a synchronization measure for chaotic, noisy and stochastic time series

A Delay Vector Variance based Approach for Detecting and Isolating the Non-linearity Induced Oscillations in Control Loops

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