http://web.mit.edu/~ppurdon/www/source-imaging.pdf

EEG source imaging

Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness.

In this tutorial review, we detail both the rationale for as well as the implementation of a set of analyses of surface-recorded event-related potentials (ERPs) that uses the reference-free spatial (i.e. topographic) information available from high-density electrode montages to render statistical information concerning modulations in response strength, latency, and topography both between and within experimental conditions. In these and other ways these topographic analysis methods allow the experimenter to glean additional information and neurophysiologic interpretability beyond what is available from canonical waveform analyses. In this tutorial we present the example of somatosensory evoked potentials (SEPs) in response to stimulation of each hand to illustrate these points. For each step of these analyses, we provide the reader with both a conceptual and mathematical description of how the analysis is carried out, what it yields, and how to interpret its statistical outcome. We show that these topographic analysis methods are intuitive and easy-to-use approaches that can remove much of the guesswork often confronting ERP researchers and also assist in identifying the information contained within high-density ERP datasets.

/pdf/topographic-erp-analyses-a-step-by-step-tutorial-review-4959e4ys2o.pdf

Topographic ERP Analyses: A Step-by-Step Tutorial Review

http://www.liralab.it/IIT_school/files/Papers/NeuroimageDTFfMRI.pdf

Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function.

The aim of this work is to characterize quantitatively the performance of a body of techniques in the frequency domain for the estimation of cortical connectivity from high-resolution EEG recordings in different operative conditions commonly encountered in practice. Connectivity pattern estimators investigated are the Directed Transfer Function (DTF), its modification known as direct DTF (dDTF) and the Partial Directed Coherence (PDC). Predefined patterns of cortical connectivity were simulated and then retrieved by the application of the DTF, dDTF, and PDC methods. Signal-to-noise ratio (SNR) and length (LENGTH) of EEG epochs were studied as factors affecting the reconstruction of the imposed connectivity patterns. Reconstruction quality and error rate in estimated connectivity patterns were evaluated by means of some indexes of quality for the reconstructed connectivity pattern. The error functions were statistically analyzed with analysis of variance (ANOVA). The whole methodology was then applied to high-resolution EEG data recorded during the well-known Stroop paradigm. Simulations indicated that all three methods correctly estimated the simulated connectivity patterns under reasonable conditions. However, performance of the methods differed somewhat as a function of SNR and LENGTH factors. The methods were generally equivalent when applied to the Stroop data. In general, the amount of available EEG affected the accuracy of connectivity pattern estimations. Analysis of 27 s of nonconsecutive recordings with an SNR of 3 or more ensured that the connectivity pattern could be accurately recovered with an error below 7% for the PDC and 5% for the DTF. In conclusion, functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in most EEG recordings by combining high-resolution EEG techniques, linear inverse estimation of the cortical activity, and frequency domain multivariate methods such as PDC, DTF, and dDTF.

/pdf/comparison-of-different-cortical-connectivity-estimators-for-4t656mmuoe.pdf

Comparison of different cortical connectivity estimators for high-resolution EEG recordings.

The past decades have shown extraordinary progress in our ability to noninvasively image the functions of the human brain. Of particular interest is the recent trend in combining information from electrophysiological and magnetic resonance imaging, which we termed eMRI, to achieve high-resolution functional neuroimaging in both space and time domains. In this article, we review the recent progress in high-resolution functional neuroimaging, in particular the multimodal integration of electroencephalography (EEG) and magnetic resonance imaging (MRI). The state-of-the-art EEG inverse solutions based on different brain electric source models and various approaches to integrate the information from MRI are reviewed. The remaining challenges, future trends, and potential applications of the high-resolution functional neuroimaging research are discussed.

High-resolution spatio-temporal functional neuroimaging of brain activity.

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High-resolution spatio-temporal functional neuroimaging of brain activity.