Wavelet Analysis of Neuroelectric Waveforms: A Conceptual Tutorial

Samar, Vincent J.; Bopardikar, Ajit S.; Rao, Raghuveer M.; Swartz, Kenneth P.

doi:10.1006/brln.1998.2024

Cited by 296 publications

(227 citation statements)

References 25 publications

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“…Wavelet analysis allows flexible control over the resolution with which neuroelectric components and events can be localized in time and frequency. This control translates into better detection of dynamic ERP components [29].…”

Section: Continuous Wavelet Transformmentioning

confidence: 99%

Exploring non-stationarity patterns in schizophrenia: neural reorganization abnormalities in the alpha band

et al. 2017

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Objective. The aim of this paper was to characterize brain non-stationarity during an auditory oddball task in schizophrenia (SCH). The level of non-stationarity was measured in the baseline and response windows of relevant tones in SCH patients and healthy controls. Approach. Eventrelated potentials were recorded from 28 SCH patients and 51 controls. Non-stationarity was estimated in the conventional electroencephalography frequency bands by means of KullbackLeibler divergence (KLD). Relative power (RP) was also computed to assess a possible complementarity with KLD. Main results. Results showed a widespread statistically significant increase in the level of non-stationarity from baseline to response in all frequency bands for both groups. Statistically significant differences in non-stationarity were found between SCH patients and controls in beta-2 and especially in the alpha band. SCH patients showed more nonstationarity in the left parieto-occipital region during the baseline window in the beta-2 band. A leave-one-out cross validation classification study with feature selection based on binary stepwise logistic regression to discriminate between SCH patients and controls provided an accuracy of 89.87% and area under ROC of 0.9510. Significance. KLD can characterize transient neural reorganization during an attentional task in response to novelty and relevance. Our findings suggest anomalous reorganization of neural dynamics in SCH during an oddball task. The abnormal frequency-dependent modulation found in SCH patients during relevant tones is in agreement with the hypothesis of aberrant salience detection in SCH. The increase in nonstationarity in the alpha band during the active task supports the notion that this band is involved in top-down processing. The baseline differences in the beta-2 band suggest that hyperactivation of the default mode network during attention tasks may be related to SCH symptoms. Furthermore, the binary stepwise logistic regression procedure selected features from both KLD and RP, supporting the idea that these measures can be complementary.

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Section: Continuous Wavelet Transformmentioning

confidence: 99%

Exploring non-stationarity patterns in schizophrenia: neural reorganization abnormalities in the alpha band

et al. 2017

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“…The discrete wavelet transform (DWT), and its use as a multiresolution analysis (MRA) tool, has been widely described in the literature (Jawerth and Sweldens, 1994;Hess-Nielsen and Wickerhauser, 1996;Unser and Aldroubi, 1996;Blinowska and Durka, 1997;Samar et al, 1999;Wilson, 2002;Bradley and Wilson, 2004;Wilson, 2004;Zhang et al, 2004;Bradley and Wilson, 2005;Zhang et al, 2005). In summary, the DWT is a form of digital filtering capable of deconstructing a signal into its component scales (frequency ranges), and then detailing how each scale evolves over time.…”

Section: The Over-complete Discrete Wavelet Transformmentioning

confidence: 99%

“…It should be noted that both digital implementations of the classical analogue filter types (such as Butterworth, Chebyshev and Elliptic), and the linear phase FIR filters implemented in ABR by Urbach and Pratt (1986), Pratt et al (1989), Pratt et al (1991) and Kawasaki and Inada (1993), can not be designed to have all three of these properties simultaneously. In addition, the type of DWT used in our study -an over complete discrete wavelet transform (OCDWT) -is computationally less demanding than applying multiple conventional digital filters (which becomes equivalent to a continuous wavelet transform [CWT] [Samar et al, 1999]). Whilst, we have chosen to use an OCDWT in this study, for the reasons outlined above, it is still reasonable to expect that a decomposition of a signal using other digital filters, such as those used by Urbach and Pratt (1986), Pratt et al (1989) Pratt et al (1991), and Kawasaki and Inada (1993), would provide similar results to those obtained using the OCDWT of a signal, provided the corner frequencies of each sub-band were the same.…”

Section: The Over-complete Discrete Wavelet Transformmentioning

confidence: 99%

“…Whilst a decrease in stimulus level results in a decrease in the amplitude of all three components, the amplitude ratio remains relatively steady. As a result, when the stimulus level approaches threshold, the high frequency component approaches the noise floor first, followed by the mid frequency component, until in some cases only the low frequency component remains (Suzuki et al, 1982;Suzuki et al, 1986;Delgado and Ö zdamar, 1994;Suzuki et al, 1994;Yokoyama et al, 1994;Samar et al, 1999;Wilson, 2004).…”

Section: Similarities and Differences In The Dual Structure Of The Abmentioning

confidence: 99%

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On the dual structure of the auditory brainstem response in dogs

Wilson

Bailey

Balke

et al. 2006

Clinical Neurophysiology

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Objective: To use the over-complete discrete wavelet transform (OCDWT) to further examine the dual structure of auditory brainstem response (ABR) in the dog. Methods: ABR waveforms recorded from 20 adult dogs at supra-threshold (90 and 70 dBnHL) and threshold (0-15 dBSL) levels were decomposed using a six level OCDWT and reconstructed at individual scales (frequency ranges) A6 (0-391 Hz), D6 (391-781 Hz), and D5 (781-1563 Hz). Results: At supra-threshold stimulus levels, the A6 scale (0-391 Hz) showed a large amplitude waveform with its prominent wave corresponding in latency with ABR waves II/III; the D6 scale (391-781 Hz) showed a small amplitude waveform with its first four waves corresponding in latency to ABR waves I, II/III, V, and VI; and the D5 scale (781-1563 Hz) showed a large amplitude, multiple peaked waveform with its first six waves corresponding in latency to ABR waves I, II, III, IV, V, and VI. At threshold stimulus levels (0-15 dBSL), the A6 scale (0-391 Hz) continued to show a relatively large amplitude waveform, but both the D6 and D5 scales (391-781 and 781-1563 Hz, respectively) now showed relatively small amplitude waveforms. Conclusions: A dual structure exists within the ABR of the dog, but its relative structure changes with stimulus level. Significance: The ABR in the dog differs from that in the human both in the relative contributions made by its different frequency components, and the way these components change with stimulus level.

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“…Wavelets can be used for the joint time-frequency analysis of EEG signals and they provide more robust measures for the detection and analysis of ERP components (Blanco et al, 1998). Samar et al (1999) and Quian Quiroga et al (2001) have presented evidence that wavelets may improve the extraction and analysis of ERP waveforms. The applications of wavelets to ERPs are broad ranging, including joint time-frequency analysis of ERPs (Samar et al, 1992), artifact removal (Jiang et al, 2007) and event detection (Demiralp et al, 1999;Samar et al, 1995).…”

Section: Introductionmentioning

confidence: 99%

A wavelet based algorithm for the identification of oscillatory event-related potential components

Aniyan¹,

Philip²,

Samar

et al. 2014

Journal of Neuroscience Methods

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Event Related Potentials (ERPs) are very feeble alterations in the ongoing Electroencephalogram (EEG) and their detection is a challenging problem. Based on the unique time-based parameters derived from wavelet coefficients and the asymmetry property of wavelets a novel algorithm to separate ERP components in single-trial EEG data is described. Though illustrated as a specific application to N170 ERP detection, the algorithm is a generalized approach that can be easily adapted to isolate different kinds of ERP components. The algorithm detected the N170 ERP component with a high level of accuracy. We demonstrate that the asymmetry method is more accurate than the matching wavelet algorithm and t-CWT method by 48.67 and 8.03 percent respectively. This paper provides an off-line demonstration of the algorithm and considers issues related to the extension of the algorithm to real-time applications.

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Wavelet Analysis of Neuroelectric Waveforms: A Conceptual Tutorial

Cited by 296 publications

References 25 publications

Exploring non-stationarity patterns in schizophrenia: neural reorganization abnormalities in the alpha band

Exploring non-stationarity patterns in schizophrenia: neural reorganization abnormalities in the alpha band

On the dual structure of the auditory brainstem response in dogs

A wavelet based algorithm for the identification of oscillatory event-related potential components

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