Quantifying additive evoked contributions to the event-related potential (original) (raw)
Related papers
Journal of Neurophysiology, 2006
Differentially variable component analysis: identifying multiple evoked components using trial-to-trial variability. . Electric potentials and magnetic fields generated by ensembles of synchronously active neurons, either spontaneously or in response to external stimuli, provide information essential to understanding the processes underlying cognitive and sensorimotor activity. Interpreting recordings of these potentials and fields is difficult because detectors record signals simultaneously generated by various regions throughout the brain. We introduce a novel approach to this problem, the differentially variable component analysis (dVCA) algorithm, which relies on trial-to-trial variability in response amplitude and latency to identify multiple components. Using simulations we demonstrate the importance of response variability to component identification, the robustness of dVCA to noise, and its ability to characterize single-trial data. We then compare the source-separation capabilities of dVCA with those of principal component analysis and independent component analysis. Finally, we apply dVCA to neural ensemble activity recorded from an awake, behaving macaque-demonstrating that dVCA is an important tool for identifying and characterizing multiple components in the single trial.
An Analytical Framework for Modeling Evoked and Event-Related Potentials
International Journal of Bifurcation and Chaos, 2004
The presented work introduces shortly a novel segmentation method and a modeling approach for multivariate quasi-stationary data. The combination of both allows the extraction of low-dimensional models from multi-dimensional data. The segmentation method is applied both to event-related potentials and fields and early auditory evoked potentials. Additionally, the early auditory wave P a is modeled by a two-dimensional dynamical system. The segmentation method detects ERP-and ERF-components and early auditory waves objectively, which illustrates the independence of the segmentation method from the number of segments. Additionally, we find a common topology of wave P a, which indicates a common underlying attractor in the brain.
Independent component analysis of single trial evoked brain reponses: is it reliable?
2005
Single-trials in event-related potential (ERP) experiments consists of electroencephalographic (EEG) recordings of brain activity time-locked to experimental events. These are usually averaged across a set of similar or identical events to increase their signal/noise ratio relative to non-phase locked EEG activity and non-brain artifacts, regardless of the fact that response activity may vary widely across trials in time course and scalp distribution. Averaging thus may not be suitable for investigating neuron brain dynamics involving transitory and intermittent subject cognitive states. Analysis of single ERP epochs, on the other hand, while ideal, suffers from confusions caused by significant EEG artifacts associated with blinks, eye-movements, and muscle noise, by large non-phase locked background EEG activities, and by the wide variability in latencies and amplitudes of ERP waveforms from trial to trial. This study introduces a new visualization tool, the 'ERP image', for investigating variability in latencies and amplitudes of event-evoked responses in spontaneous EEG or MEG records. Second, we apply a new linear decomposition tool, Independent Component Analysis (ICA) [I], to multichannel single-trial EEG records to derive spatial filters that decompose single-trial EEG epochs into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain networks. We demonstrate the power of the proposed analysis and visualization tools for single-trial ERP analysis through
INDEPENDENT COMPONENT ANALYSIS OF SINGLE-TRIAL EVENT-RELATED POTENTIALS
Single-trials in event-related potential (ERP) experiments consists of electroencephalographic (EEG) recordings of brain activity time-locked to experimental events. These are usually averaged across a set of similar or identical events to increase their signal/noise ratio relative to non-phase locked EEG activity and non-brain artifacts, regardless of the fact that response activity may vary widely across trials in time course and scalp distribution. Averaging thus may not be suitable for investigating neuron brain dynamics involving transitory and intermittent subject cognitive states. Analysis of single ERP epochs, on the other hand, while ideal, suffers from confusions caused by significant EEG artifacts associated with blinks, eye-movements, and muscle noise, by large non-phase locked background EEG activities, and by the wide variability in latencies and amplitudes of ERP waveforms from trial to trial. This study introduces a new visualization tool, the 'ERP image', for investigating variability in latencies and amplitudes of event-evoked responses in spontaneous EEG or MEG records. Second, we apply a new linear decomposition tool, Independent Component Analysis (ICA) [I], to multichannel single-trial EEG records to derive spatial filters that decompose single-trial EEG epochs into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain networks. We demonstrate the power of the proposed analysis and visualization tools for single-trial ERP analysis through
Modeling extended sources of event-related potentials using anatomical and physiological constraints
Human Brain Mapping, 1999
For the study of functional organization and reorganization of the human cortex by means of electromagnetic source imaging, a measure of the location and spatial extent of neural sources is of interest. This study evaluates the cortical patch method (CPM), an iterative procedure introduced by Lü tkenhö ner et al. [1995] that models EEG/MEG activity by means of extended cortical patches. Anatomical information is used to constrain estimates of location and extent of neural sources that generate the measured evoked potential. Whereas minimum norm approaches use mathematical constraints to solve the ambiguity of the inverse problem, the CPM introduces constraints based on anatomical and physiological knowledge about neural mass activity. In order to test the proposed method, the simulated activity in an artificial sulcus was subjected to the CPM. The results show that even activity on opposing walls of a sulcus can be well reconstructed. The simulations demonstrate the usefulness and limits of the CPM in estimating the spatial extent of neural sources in the cerebral cortex. As an example, an application of the method on experimental somatosensory evoked potentials is presented in the Appendix.
Proceedings of the National Academy of Sciences, 2005
The brain acts as an integrated information processing system, which methods in cognitive neuroscience have so far depicted in a fragmented fashion. Here, we propose a simple and robust way to integrate functional MRI (fMRI) with single trial event-related potentials (ERP) to provide a more complete spatiotemporal characterization of evoked responses in the human brain. The idea behind the approach is to find brain regions whose fMRI responses can be predicted by paradigm-induced amplitude modulations of simultaneously acquired single trial ERPs. The method was used to study a variant of a two-stimulus auditory target detection (oddball) paradigm that manipulated predictability through alternations of stimulus sequences with random or regular target-to-target intervals. In addition to electrophysiologic and hemodynamic evoked responses to auditory targets per se, single-trial modulations were expressed during the latencies of the P2 (170-ms), N2 (200-ms), and P3 (320-ms) components and predicted spatially separated fMRI activation patterns. These spatiotemporal matches, i.e., the prediction of hemodynamic activation by time-variant information from single trial ERPs, permit inferences about regional responses using fMRI with the temporal resolution provided by electrophysiology.
IEEE Transactions on Biomedical Engineering, 2000
This paper describes the theoretical background of a new data-driven approach to encephalographic single-trial (ST) data analysis. Temporal constrained source extraction using sparse decomposition identifies signal topographies that closely match the shape characteristics of a reference signal, one response for each ST. The correlations between these ST topographies are computed for formal Correlation Matrix Analysis (CMA) based on Random Matrix Theory (RMT). The RMT-CMA provides clusters of similar ST topologies in a completely unsupervised manner. These patterns are then classified into deterministic set and noise using well established RMT results. The efficacy of the method is applied to EEG and MEG data of somatosensory evoked responses (SERs). The results demonstrate that the method can recover brain signals with time course resembling the reference signal and follow changes in strength and/or topography in time by simply stepping the reference signal through time.
Independent Component Analysis of Event-Related Potentials
cogsci-online.ucsd.edu
Independent component analysis (ICA) is a potentially powerful tool for analyzing event-related potentials (ERPs), one of the most popular measures of brain function in cognitive neuroscience. Based on the statistics of the electroencephalogram (EEG), from which ERPs are derived, ICA may be able to extract multiple, functionally distinct sources of an ERP generated by disparate regions of cerebral cortex. Extracting such sources greatly increases the informativeness of ERPs by providing a cleaner, less ambiguous measure of source activity and by facilitating the identification of this activity across different experimental paradigms. The main purpose of this review article is to explain the logic of ICA, to illustrate how ICA could in principle extract spatiotemporally overlapping ERP sources, and to review evidence that ICA is a well motivated methodology that can extract latent ERP sources in practice. In addition, we close the article by noting potential problems with ICA and by comparing it to three alternative methods for extracting ERP sources/components: spatial principal component analysis, source localization, and temporal principal component analysis.
Dynamic causal modelling of evoked potentials: A reproducibility study
NeuroImage, 2007
Dynamic causal modelling (DCM) has been applied recently to eventrelated responses (ERPs) measured with EEG/MEG. DCM attempts to explain ERPs using a network of interacting cortical sources and waveform differences in terms of coupling changes among sources. The aim of this work was to establish the validity of DCM by assessing its reproducibility across subjects. We used an oddball paradigm to elicit mismatch responses. Sources of cortical activity were modelled as equivalent current dipoles, using a biophysical informed spatiotemporal forward model that included connections among neuronal subpopulations in each source. Bayesian inversion provided estimates of changes in coupling among sources and the marginal likelihood of each model. By specifying different connectivity models we were able to evaluate three different hypotheses: differences in the ERPs to rare and frequent events are mediated by changes in forward connections (F-model), backward connections (B-model) or both (FB-model).