Wavelet entropy analysis of event-related potentials indicates modality-independent theta dominance

Yordanova, Juliana; Kolev, Vasil; Rosso, Osvaldo A.; Schürmann, Martin; Sakowitz, Oliver W.; Özgören, Murat; Başar, Erol

doi:10.1016/s0165-0270(02)00095-x

Cited by 115 publications

(66 citation statements)

References 48 publications

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“…This state was consistently determined by synchronized theta oscillations and had a specific anterior distribution. The stimulus-locked theta state may reflect a processing stage during which interfering activations from other frequency networks are minimized (Yordanova et al, 2002).…”

Section: Event-related Theta Band Changesmentioning

confidence: 99%

“…Some studies indicate that event-related theta activity may not only contribute directly to P300 waveform expression, but may also modify P300 via other processes in the theta frequency channel of the EEG (Kolev et al, 1994;Yordanova and Kolev, 1996). Modality independent transient theta-dominated state may reflect a processing stage that is obligatory for stimulus evaluation (Yordanova et al, 2002). Karakas et al (2000b)hypothesized that the delta response represents degrees of consciousness involved during conscious stimulus evaluation and memory updating, and that the theta oscillation represents different levels and forms of attention.…”

Section: Introductionmentioning

confidence: 99%

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Delta and theta oscillations as risk markers in adolescent offspring of alcoholics

Rangaswamy

Jones

Porjesz

et al. 2007

International Journal of Psychophysiology

117

136

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Background-Visual P300 is consistently lower in alcohol dependent individuals, their offspring and subjects at risk. Delta and theta event related oscillations (ERO) are the major contributors to the P300 signal. The total and evoked power in delta and theta bands in the 300 to 700 millisecond post-stimulus window (corresponding to the zone of P3 maxima) was compared between adolescent offspring of alcoholics (high-risk) and age-matched normal controls (low-risk), to assess the utility of the risk markers.Methods-EEG was recorded during the performance of visual oddball task. The S-Transform algorithm decomposed the EEG signals into different frequency bands and the group differences in total and evoked power in the oscillatory responses during the P300 time window (300 to 700 ms) were analyzed using a multivariate design. Similar analysis was performed on P300 peak amplitudes for the target.Results-The high-risk group showed significantly lower parietal post-stimulus evoked and total power in the delta band for targets. A decrease in total power was seen centrally and parietally in theta band. The P300 peak amplitude in the parietal electrodes was also significantly lower in the high-risk group.Conclusions-The decreased total theta power and total and evoked delta power for visual targets in high risk individuals may serve as an endophenotypic marker in the development of alcoholism and other disinhibitory disorders. The differences seen between the offspring of alcoholics and controls may have a cholinergic basis. The ERO measures appear to be more robust than the P300 amplitude in differentiating the groups.

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Section: Event-related Theta Band Changesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Delta and theta oscillations as risk markers in adolescent offspring of alcoholics

Rangaswamy

Jones

Porjesz

et al. 2007

International Journal of Psychophysiology

117

136

View full text Add to dashboard Cite

show abstract

“…The wavelet entropy HWT can reflect the information amount of image, the larger HWT is, the more abundant detail component of image contains. At present, the wavelet entropy has become a main feature extraction method of image [9][10][11].…”

Section: Feature Extraction Methods Based On Wavelet Kurtosismentioning

confidence: 99%

Recognition method of metal fracture images based on Wavelet kurtosis and Relevance vector machine

Sun

Yan

2016

MATEC Web of Conferences

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Abstract. The useful information extracted from fracture images is the most fundamental problem of quantitative analysis and intelligent diagnosis of metal fracture. The pattern recognition or classification is the critical issue of failure analysis of metal fracture. In this paper, combining wavelet transform, kurtosis with relevance vector machine (RVM), a new recognition method based on wavelet kurtosis and RVM, which is named wavelet kurtosis-RVM, is proposed. In the proposed method, wavelet kurtosis is used as a feature vector, and RVM as a classifier. The proposed method has been successfully applied to the recognition of fracture images. The proposed method is also compared with the wavelet entropy-RVM recognition method and wavelet kurtosis-SVM recognition method. The experiment result shows that the proposed method is very effective. Compared with the Wavelet entropy-RVM recognition method, Wavelet kurtosis is more sensitive to the texture change of metal fracture and suitable for feature extraction of metal fracture. Compared with the Wavelet kurtosis-SVM recognition method, The proposed method and Wavelet kurtosis-SVM recognition method have the same good recognition rate. However, in the recognition speed, the Wavelet kurtosis-RVM recognition method is obviously superior to the Wavelet kurtosis-SVM recognition method, especially in the increase of training samples.

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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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Wavelet entropy analysis of event-related potentials indicates modality-independent theta dominance

Cited by 115 publications

References 48 publications

Delta and theta oscillations as risk markers in adolescent offspring of alcoholics

Delta and theta oscillations as risk markers in adolescent offspring of alcoholics

Recognition method of metal fracture images based on Wavelet kurtosis and Relevance vector machine

Continuous time wavelet entropy of auditory evoked potentials

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