EEG analysis using wavelet-based information tools

Rosso, Osvaldo A.; Martín, María Teresa; Figliola, A.; Keller, Karsten; Plastino, A.

doi:10.1016/j.jneumeth.2005.10.009

Cited by 197 publications

(185 citation statements)

References 42 publications

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“…PSDs were averaged for each channel and subject to compute the mean power spectrum. Then, the RSP was calculated in the classical frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta1 (13)(14)(15)(16)(17)(18)(19), beta2 (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (30-64 Hz). If we denote the power at each frequency band as P j , j = {delta, theta, alpha, beta1, beta2, gamma}, then, the RSP can be represented as a probability distribution,…”

Section: B Definition Of the Spectral Entropiesmentioning

confidence: 99%

“…In a similar context, previous studies have successfully applied several quantifiers based on extensive (Shannon) and nonextensive (Tsallis, generalized escort-Tsallis and Rényi) information measures to analyze EEG signals [17]− [20]. These non-extensive measures are controlled by the parameter q, which is modified to emphasize particular characteristics of the associated dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Spontaneous MEG Activity in Patients with Alzheimer's Disease using Spectral Entropies

Poza

Hornero

Abásolo

et al. 2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-The aim of this study was to explore the ability of several spectral entropies to discriminate between spontaneous magnetoencephalographic (MEG) oscillations from 20 Alzheimer's disease (AD) patients and 21 controls. Hence, the relative spectral power (RSP) in classical frequency bands was calculated from the averaged power spectral density. Given the fact that the RSP can be viewed as a probability distribution function, the Shannon spectral entropy, Tsallis spectral entropy, generalized escort-Tsallis spectral entropy and Rényi spectral entropy were calculated from the RSP. Significant differences for each parameter were assessed with MannWhitney U test, whereas classification performance was studied using binary logistic regression. Results revealed an increase in the RSP of control subjects at beta and gamma bands, while AD patients showed an increase in the RSP values at delta and theta bands. Entropic quantifiers obtained statistically significant lower values for AD patients than for controls. This issue suggests a significant decrease in irregularity of AD patients' MEG activity.

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Section: B Definition Of the Spectral Entropiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of Spontaneous MEG Activity in Patients with Alzheimer's Disease using Spectral Entropies

Poza

Hornero

Abásolo

et al. 2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…EEG can not only reflect the characteristics of brain activity itself, but also yield clues concerning the underlying associated neural dynamics (Rosso et al 2006). It can be regarded as the activities of ensembles of generators producing oscillations in several frequency ranges.…”

Section: Introductionmentioning

confidence: 99%

“…The information processing in the brain is reflected by dynamical changes in electrical activity in time, frequency and space (Rosso et al 2006), which can be measured by EEG. Therefore, the concomitant studies require methods capable of describing the qualitative and quantitative variation of EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale order recurrence quantification analysis of EEG signals evoked by manual acupuncture in healthy subjects

Wang

Bian

et al. 2012

Cogn Neurodyn

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To explore the effects of manual acupuncture (MA) on brain activities, we design an experiment that acupuncture at acupoint ST36 of right leg with four different frequencies to obtain electroencephalograph (EEG) signals. Many studies have demonstrated that the complexity of EEG can reflect the states of brain function, so we propose to adopt order recurrence quantification analysis combined with discrete wavelet transform, to analyze the dynamical characteristics of different EEG rhythms under acupuncture, further to explore the effects of MA on the complexity of brain activities from multi-scale point of view. By analyzing the complexity of five EEG rhythms, it is found that the complexity of delta rhythm during acupuncture is lower than before acupuncture, and for alpha rhythm that is higher, but for beta, theta and gamma rhythms there are no obvious changes. All of those effects are especially obvious during acupuncture with frequency of 200 times/min. Furthermore, the determinism extracted from delta, alpha and gamma rhythms can be regarded as a characteristic parameter to distinguish the state acupuncture at 200 times/min and the state before acupuncture. These results can provide a theoretical support for selecting appropriate acupuncture frequency for patients in clinical, and the proposed methods have the potential of exploring the effects of acupuncture on brain activities.

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“…Therefore, wavelet based analysis has come into prominence due to flexible time-frequency atoms which are regarded as proper candidates to uncover the common mechanism of neural structures. The limitations associated with the time-frequency resolution may be overcome by the method given in [7][8][9][10][11][12] which is based on discrete time wavelet decomposition introduced as orthogonal discrete wavelet transform (ODWT) [1,13]. This method decomposes an observed signal into definite frequency bands without losing time evolution held by the wavelet coefficients.…”

Section: Introductionmentioning

confidence: 99%

Continuous time wavelet entropy of auditory evoked potentials

Cek

Özgören

Savacı

2010

Computers in Biology and Medicine

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a b s t r a c tIn this paper, the continuous time wavelet entropy (CTWE) of auditory evoked potentials (AEP) has been characterized by evaluating the relative wavelet energies (RWE) in specified EEG frequency bands. Thus, the rapid variations of CTWE due to the auditory stimulation could be detected in post-stimulus time interval. This approach removes the probability of missing the information hidden in short time intervals. The discrete time and continuous time wavelet based wavelet entropy variations were compared on non-target and target AEP data. It was observed that CTWE can also be an alternative method to analyze entropy as a function of time.

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EEG analysis using wavelet-based information tools

Cited by 197 publications

References 42 publications

Analysis of Spontaneous MEG Activity in Patients with Alzheimer's Disease using Spectral Entropies

Analysis of Spontaneous MEG Activity in Patients with Alzheimer's Disease using Spectral Entropies

Multi-scale order recurrence quantification analysis of EEG signals evoked by manual acupuncture in healthy subjects

Continuous time wavelet entropy of auditory evoked potentials

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