Phase Correlations in Human EEG Signal: A Case Study

Sandha, G.S.; Singh, Pankaj; Oberoi, Nimish H; Nagchoudhuri, D.

doi:10.1109/delta.2004.10063

Cited by 2 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since then, EEG signal analysis using the bispectrum and its normalized form, the bicoherence, have been used to profile changes in the EEG due to the nonlinear interaction among its many sinusoidal components (Sigl and Chamoun 1994;Shils 1995;Varela et al 2001;Bronzino 2006). Most of these works investigated and qualified the nonlinear couplings of the EEG in a variety of physiological and pathological states, e.g., waking, sleep, and seizure, (Barnett et al 1971;Dummermuth et al 1971;Schanze 1997;Bullock et al 1997;Witte et al 1997;Akgül et al 2000;Andrzejak et al 2001;Sandha et al 2004), depending on different recording regions (Andrzejak et al 2001;Bianchi et al 2004), resulting from visual stimulation (Shils et al 1996;Schanze 1997;Bianchi et al 2004), and on the basis of analytical signals (Witte and Schack 2003). Changes in the EEG bicoherences under different physiologic conditions (Ning 1988;Ning and Bronzino 1989;Boeijinga and Lopes da Silva 1989;Ning andBronzino 1990, 1991) and during alternating period of consciousness and unconsciousness (Gajraj et al 1998) have been evaluated.…”

Section: Introductionmentioning

confidence: 99%

Canonical bicoherence analysis of dynamic EEG data

Thomson

2009

J Comput Neurosci

View full text Add to dashboard Cite

Bicoherence has been used in quantifying quadratic phase coupling (QPC) in electroencephalography (EEG) signals. However, for high-dimensional EEG signals, the calculations of traditional auto- and cross-bicoherences of signals from multiple electrodes are computationally very expensive. This has been compounded by the recognition of the non-stationary character of EEG signals. This paper introduces a new approach, the time-varying canonical bicoherence (CBC) by short-time weighted Fourier transforms, for analyzing QPC nonlinearities of dynamic EEG signals. This new method shows both computational efficiency and simple interpretation of estimated canonical bicoherences. The canonical bicoherence analysis of EEG records, during a human visual stimulus-driven cognitive process, put into evidence of quadratic phase couplings of Beta waves and Delta waves in the frontal regions.

show abstract

Section: Introductionmentioning

confidence: 99%

Canonical bicoherence analysis of dynamic EEG data

Thomson

2009

J Comput Neurosci

View full text Add to dashboard Cite

show abstract

EEG Signal Analysis: A Survey

et al. 2008

View full text Add to dashboard Cite

The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They are highly random in nature and may contain useful information about the brain state. However, it is very difficult to get useful information from these signals directly in the time domain just by observing them. They are basically non-linear and nonstationary in nature. Hence, important features can be extracted for the diagnosis of different diseases using advanced signal processing techniques. In this paper the effect of different events on the EEG signal, and different signal processing methods used to extract the hidden information from the signal are discussed in detail. Linear, Frequency domain, time - frequency and non-linear techniques like correlation dimension (CD), largest Lyapunov exponent (LLE), Hurst exponent (H), different entropies, fractal dimension(FD), Higher Order Spectra (HOS), phase space plots and recurrence plots are discussed in detail using a typical normal EEG signal.

show abstract

Phase Correlations in Human EEG Signal: A Case Study

Cited by 2 publications

References 6 publications

Canonical bicoherence analysis of dynamic EEG data

Canonical bicoherence analysis of dynamic EEG data

EEG Signal Analysis: A Survey

Contact Info

Product

Resources

About