Bispectral analysis of visual interactions in humans

Shils, Jay L.; Litt, M.; Skolnick, Brett E.; Stecker, Mark M.

doi:10.1016/0013-4694(95)00230-8

Cited by 58 publications

(41 citation statements)

References 18 publications

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“…Relatively few studies have thus far investigated nonlinear (CF) phase coupling (Jeffrey and Chamoun, 1994;Shils et al, 1996;Schanze and Eckhorn, 1997;von Stein et al, 2000;Schack et al, 2002) or nested oscillations (Chrobak and Buzsaki, 1998;Schack et al, 2002;Bruns and Eckhorn, 2004;Vanhatalo et al, 2004). We have shown here that, among cortical oscillations, CF phase interactions are also found in the form of 1:m-type CF phase synchrony.…”

Section: Discussionmentioning

confidence: 53%

“…Bispectral measures such as bicoherence (Dumermuth et al, 1971;Jeffrey and Chamoun, 1994) have been used to detect nonlinear cross-frequency (CF) phase coupling in the cat cortex (Schanze and Eckhorn, 1997;von Stein et al, 2000) and in human electroencephalograms (EEGs) (Jeffrey and Chamoun, 1994;Shils et al, 1996;Schack et al, 2002). However, like conventional coherence (cf.…”

Section: Introductionmentioning

confidence: 99%

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Phase Synchrony among Neuronal Oscillations in the Human Cortex

Palva¹,

Kaila²

2005

J. Neurosci.

585

518

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Synchronization of neuronal activity, often associated with network oscillations, is thought to provide a means for integrating anatomically distributed processing in the brain. Neuronal processing, however, involves simultaneous oscillations in various frequency bands. The mechanisms involved in the integration of such spectrally distributed processing have remained enigmatic. We demonstrate, using magnetoencephalography, that robust cross-frequency phase synchrony is present in the human cortex among oscillations with frequencies from 3 to 80 Hz. Continuous mental arithmetic tasks demanding the retention and summation of items in the working memory enhanced the cross-frequency phase synchrony among ␣ (ϳ10 Hz), ␤ (ϳ20 Hz), and ␥ (ϳ30-40 Hz) oscillations. These tasks also enhanced the "classical" within-frequency synchrony in these frequency bands, but the spatial patterns of ␣, ␤, and ␥ synchronies were distinct and, furthermore, separate from the patterns of cross-frequency phase synchrony. Interestingly, an increase in task load resulted in an enhancement of phase synchrony that was most prominent between ␥-and ␣-band oscillations. These data indicate that crossfrequency phase synchrony is a salient characteristic of ongoing activity in the human cortex and that it is modulated by cognitive task demands. The enhancement of cross-frequency phase synchrony among functionally and spatially distinct networks during mental arithmetic tasks posits it as a candidate mechanism for the integration of spectrally distributed processing.

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Section: Discussionmentioning

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Phase Synchrony among Neuronal Oscillations in the Human Cortex

Palva¹,

Kaila²

2005

J. Neurosci.

585

518

View full text Add to dashboard Cite

show abstract

“…Bispectral measures such as bicoherence (Dumermuth et al, 1971) have been used to detect non-linear coupling in human EEG (Jeffrey and Chamoun, 1994;Shils et al, 1996). The key thing about non-linear coupling is that it induces dependencies among different frequencies.…”

Section: Non-linear Methodsmentioning

confidence: 99%

“…Bicoherence or the cross-bi-spectrum g uuy (ω 1 ,ω 2 ) is the third-order cross-poly-spectrum and implies a non-zero secondorder kernel or transfer function. See Friston (2000), Jeffrey and Chamoun (1994) and Shils et al (1996) for examples of detecting non-linear coupling with bi-spectral analyses and Priestley (1988) for the mathematical background.…”

Section: Generalised Convolution Modelsmentioning

confidence: 99%

Dynamic causal modelling of induced responses

2008

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This paper describes a dynamic causal model (DCM) for induced or spectral responses as measured with the electroencephalogram (EEG) or the magnetoencephalogram (MEG). We model the time-varying power, over a range of frequencies, as the response of a distributed system of coupled electromagnetic sources to a spectral perturbation. The model parameters encode the frequency response to exogenous input and coupling among sources and different frequencies. The Bayesian inversion of this model, given data enables inferences about the parameters of a particular model and allows us to compare different models, or hypotheses. One key aspect of the model is that it differentiates between linear and non-linear coupling; which correspond to within and betweenfrequency coupling respectively. To establish the face validity of our approach, we generate synthetic data and test the identifiability of various parameters to ensure they can be estimated accurately, under different levels of noise. We then apply our model to EEG data from a faceperception experiment, to ask whether there is evidence for non-linear coupling between early visual cortex and fusiform areas.

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“…Gajraj et al [11] applied the EEG bispectrum in order to distinguish the transition from unconsciousness to consciousness. Shils et al [12] studied the interactions between the electro-cerebral activities resulting from stimulation of the left and right visual fields. They observed nonlinear interactions between visual fields by means of bispectral analysis.…”

Section: Introductionmentioning

confidence: 99%

Classification of Brain Activity in Emotional States Using HOS Analysis

Hosseini¹

2012

IJIGSP

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Abstract-This paper proposes an emotion recognition system using EEG signals and higher order spectra. A visual induction based acquisition protocol is designed for recording the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) under two emotional states of participants, calm-neutral and negatively exited. After pre-processing the signals, higher order spectra are employed to extract the features for classifying human emotions. We used Genetic Algorithm (GA) and Support vector machine (SVM) for optimum features selection for the classifier. In this research, we achieved an average accuracy of 82.32% for the two emotional states using Linear Discriminant Analysis (LDA) classifier. We concluded that, HOS analysis could be an accurate tool in the assessment of human emotional states. We achieved to same results compared to our previous studies.

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Bispectral analysis of visual interactions in humans

Cited by 58 publications

References 18 publications

Phase Synchrony among Neuronal Oscillations in the Human Cortex

Phase Synchrony among Neuronal Oscillations in the Human Cortex

Dynamic causal modelling of induced responses

Classification of Brain Activity in Emotional States Using HOS Analysis

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