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

Abstract

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.

Single trial estimation of event‐related potential components using spatiotemporal filtering and artificial bee colony optimized Gaussian kernel mixture model

“…After the optimum values for the first kernel are found, we suppose this kernel as the final response (r 1 ) and calculate the amplitude of ERP component using Equation (7). Then a Gaussian priory kernel is built with the mean and variance found by the search algorithm, and the amplitude is calculated.…”

“…The first group consists of methods that use a single channel to extract the ERP components, that is, maximum likelihood, 4 match filter, 5 Residue iteration decomposition 6 and wavelet. 7 The main fact that should be considered when using single channel methods is that the user needs physiological information to choose the best channel location. So, if we want to extract more than one component with different origins, these methods cannot work well.…”

Surrogate-Based Artifact Removal From Single-Channel EEG

“…The symlets are orthogonal functions, nearly symmetrical wavelets with an oscillatory waveform and good time-frequency localization properties [16]. This makes it suitable wavelet choice for filtering and reconstructing EEG signals [17,18].…”

The anterior contralateral response improves performance in a single trial auditory oddball BMI

“…However, their strategy is not unsupervised and requires heuristic adjustments by comparing the outcomes of the denoised single-trial ERP with the raw data. More recently, Aniyana et al presented another method for detecting ERPs in single-trial EEG data [37]. The researchers claim that their new approach, based on the use of the asymmetry property of wavelets and time based properties of the target ERP components, provides higher detection accuracy, as tested in offline models, than standard matching wavelet algorithms or the t-CWT method.…”