Estimation of individual evoked potential components using iterative independent component analysis

Abstract

Independent component analysis (ICA) has been successfully employed in the study of single-trial evoked potentials (EPs). In this paper, we present an iterative temporal ICA methodology that processes multielectrode single-trial EPs, one channel at a time, in contrast to most existing methodologies which are spatial and analyze EPs from all recording channels simultaneously. The proposed algorithm aims at enhancing individual components in an EP waveform in each single trial, and relies on a dynamic template to guide EP estimation. To quantify the performance of this method, we carried out extensive analyses with artificial EPs, using different models for EP generation, including the phase-resetting and the classical additive-signal models, and several signal-to-noise ratios and EP component latency jitters. Furthermore, to validate the technique, we employed actual recordings of the auditory N100 component obtained from normal subjects. Our results with artificial data show that the proposed procedure can provide significantly better estimates of the embedded EP signals compared to plain averaging, while with actual EP recordings, the procedure can consistently enhance individual components in single trials, in all subjects, which in turn results in enhanced average EPs. This procedure is well suited for fast analysis of very large multielectrode recordings in parallel architectures, as individual channels can be processed simultaneously on different processors. We conclude that this method can be used to study the spatiotemporal evolution of specific EP components and may have a significant impact as a clinical tool in the analysis of single-trial EPs.

Schizophrenia Assessment Using Single-Trial Analysis of Brain Activity

“…Contrary to all other methods, we have proposed an alternative methodology [42,43] that can extract individual components out of the complete EP waveform. The method analyzes single-trial EPs based on independent component analysis (ICA) and relies on the hypothesis that brain activity resulting from an experimental stimulus is independent from neurophysiological artifacts and background activity [32,44,45].…”

“…In particular, we compared [46] the iterative ICA (iICA)-based single-trial analysis against traditional ensemble averaging using synthetic and real data from normal subjects and found that the iICA method provides improved estimates of the N100 component. We also showed that these findings are independent of the model assumed for EP generation and remain true for both the phase-resetting and additive models.…”

“…All single trials obtained from a particular channel are processed in the following seven steps [42,43] 4. Set to zero those ICs with correlation less than a predefined threshold r th .…”

A Bayesian method to estimate single-trial event-related potentials with application to the study of the P300 variability

“…To simulate different SNR conditions, two groups of 50 synthetic sweeps were generated. The first group comprised sweeps having a SNR uniformly distributed in the interval [0.2-0.4], while the second one presented SNR levels in the interval [1-1.2]; a similar procedure can be found in several works (e.g., Iyer and Zouridakis, 2007;Zouridakis et al, 2007). For instance, in the simulated dataset where the SNR varied uniformly between 0.2 and 0.4, the average power of the noise was from 2.5 to 5 times greater than the power of the useful signal.…”

Topographic phase maps using iterative independent component analysis

“…In the present study, we follow a different approach that is based on our recent work [4,15] with independent component analysis (ICA) of single trial EPs. The method developed, termed iterative ICA (iICA), is an iterative implementation of the information maximization algorithm originally proposed by Makeig et al [6].…”