Estimation of individual evoked potential components using iterative independent component analysis.

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.