Multiresolution Analysis of Event-Related Potentials by Wavelet Decomposition

Samar, Vincent J.; Swartz, Kenneth P.; Raghuveer, M.R.

doi:10.1006/brcg.1995.1028

Cited by 107 publications

(59 citation statements)

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“…Wavelet transform (WT) is an efficient time-frequency decomposition method (Daubechies, 1990), which has been used on ERP signals (e.g., Ademoglu et al, 1997;Kolev et al, 1997;Yordanova et al, 2000). The major advantage of WT is its multi-resolution property that employs shorter time windows for higher frequencies and longer time windows for lower frequencies-an attribute that closely matches the structural properties of ERP signals (Ademoglu et al, 1998;Samar et al, 1995). The variable timefrequency localization method therefore takes into consideration the overlapping of ERP components and provides for the efficient analysis of the transient non-stationary ERP signals (Demiralp et al, 1999).…”

Section: Neuroelectric Underpinnings Of P3a and P3bmentioning

confidence: 99%

Updating P300: An integrative theory of P3a and P3b

Polich

2007

Clinical Neurophysiology

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The empirical and theoretical development of the P300 event-related brain potential (ERP) is reviewed by considering factors that contribute to its amplitude, latency, and general characteristics. The neuropsychological origins of the P3a and P3b subcomponents are detailed, and how target/ standard discrimination difficulty modulates scalp topography is discussed. The neural loci of P3a and P3b generation are outlined, and a cognitive model is proffered: P3a originates from stimulusdriven frontal attention mechanisms during task processing, whereas P3b originates from temporalparietal activity associated with attention and appears related to subsequent memory processing. Neurotransmitter actions associating P3a to frontal/dopaminergic and P3b to parietal/norepinephrine pathways are highlighted. Neuroinhibition is suggested as an overarching theoretical mechanism for P300, which is elicited when stimulus detection engages memory operations.

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Section: Neuroelectric Underpinnings Of P3a and P3bmentioning

confidence: 99%

Updating P300: An integrative theory of P3a and P3b

Polich

2007

Clinical Neurophysiology

6,317

458

6,265

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“…The wavelet basis function and the scale are critical factors for any kind of component identification based on the CWT (Samar et al, 1995). Previous studies have shown various methods for selecting the best wavelet basis (Brechet et al, 2007;Flanders, 2002;Nielsen et al, 2006).…”

Section: Selection Of Wavelet and Scale For N170 Erpmentioning

confidence: 99%

“…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). Furthermore, features derived from wavelet coefficients (Merzagora et al, 2006;Trejo and Shensa, 1999) perform well in preprocessing (Kalayci et al, 1994) stages of classification problems using statistical learning algorithms (Abootalebi et al, 2006;Browne and Cutmore, 2002).…”

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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“…Furthermore, a more detailed analysis of topographic aspects by means of wavelet analysis may be of interest (see, e.g., Lehmann, 1989;Skrandies, 1989, for a topographic study of cognitive ERP components). In addition to ERPs with cognitive paradigms, ERP measurements in pathological conditions are also promising for the future use of wavelet analysis (Samar et al, 1995).…”

Section: Proposals For Future Researchmentioning

confidence: 99%

“…Later the same research group introduced the concept of diffusely distributed oscillatory systems in the brain (in the delta, theta, alpha, and gamma frequency ranges). In recent years, the introduction of the wavelet transform to signal analysis has brought an important advance, and this method has also been successfully applied for the analysis of brain signals according to properties of brain waves (Heinrich, Gaus, & Dickhaus, 1991;Bartnik, Blinowska, & Durka, 1992;Samar, Swartz, & Raghuveer, 1995;Ademoglu, 1995;Demiralp, Ademoglu, & Başar, 1995). The goal of the present article is multifold:…”

Section: Introductionmentioning

confidence: 99%