Taking into account latency, amplitude, and morphology: improved estimation of single-trial ERPs by wavelet filtering and multiple linear regression

Abstract

Across-trial averaging is a widely used approach to enhance the signal-to-noise ratio (SNR) of event-related potentials (ERPs). However, across-trial variability of ERP latency and amplitude may contain physiologically relevant information that is lost by across-trial averaging. Hence, we aimed to develop a novel method that uses 1) wavelet filtering (WF) to enhance the SNR of ERPs and 2) a multiple linear regression with a dispersion term (MLR d ) that takes into account shape distortions to estimate the single-trial latency and amplitude of ERP peaks. Using simulated ERP data sets containing different levels of noise, we provide evidence that, compared with other approaches, the proposed WFϩMLR d method yields the most accurate estimate of single-trial ERP features. When applied to a real laser-evoked potential data set, the WFϩMLR d approach provides reliable estimation of single-trial latency, amplitude, and morphology of ERPs and thereby allows performing meaningful correlations at single-trial level. We obtained three main findings. First, WF significantly enhances the SNR of single-trial ERPs. Second, MLR d effectively captures and measures the variability in the morphology of single-trial ERPs, thus providing an accurate and unbiased estimate of their peak latency and amplitude. Third, intensity of pain perception significantly correlates with the single-trial estimates of N2 and P2 amplitude. These results indicate that WFϩMLR d can be used to explore the dynamics between different ERP features, behavioral variables, and other neuroimaging measures of brain activity, thus providing new insights into the functional significance of the different brain processes underlying the brain responses to sensory stimuli. event-related potentials; laser-evoked potentials; single-trial analysis; multiple linear regression with dispersion term

Somatosensory spatial attention modulates amplitudes, latencies, and latency jitter of laser-evoked brain potentials

“…We performed an automated single-trial analysis using the open source single-trial toolbox "STEP1" (http://www.iannettilab.net/n1measure/), running inside the MATLAB environment. This method of analysis is based on two consecutive processing steps: 1) time-frequency wavelet filtering to enhance signal-to-noise ratio of LEPs both in single trials and averages (Hu et al 2010); and 2) multiple linear regression (MLR) to provide reliable and unbiased single-trial estimates of latency and amplitude of LEP components, as shown previously (Hu et al 2011). For detailed information, please refer to Hu et al (2010).…”

A toolbox for residue iteration decomposition (RIDE)—A method for the decomposition, reconstruction, and single trial analysis of event related potentials

“…However, it is extremely complicated and difficult to model the variation of morphology because the shape can change in many different ways. We found only very few -and rather elementary -works about modeling morphological variation (e.g., Hu et al, 2011).…”

Let's face it, from trial to trial: Comparing procedures for N170 single-trial estimation

“…Note that the model can have complexity added by including additional terms of variability (e.g. Hu et al, 2011a). This method can be freely downloaded from http://iannettilab.…”