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This chapter offers a novel method for estimating local field potentials (LFPs) via a newly developed solution for the neuroelectromagnetic inverse problem, and discusses an approach to construct individual images of brain areas that significantly differ between two or more experimental conditions. It describes the experimental paradigm used to record the electoencephalography data, and explains in more detail the alternatives used to perform statistical analysis over LFPs estimated from single trials in the temporal and spectral domains. The chapter shows a stable differentiation between the laterality of the movements within parietal areas and primary motor, and premotor cortex and the spinal muscular atrophy.

This chapter provides a practical guide to beamformer source reconstruction for electoencephalography signals and describes how the choice of head model can affect beamformer source estimations. The chapter shows that the beamformer analysis still provides only an estimate of neural activity based on recordings made outside the head, and suggests that the beamformer method does not require a priori determination of the number of possible sources. The beamformer is also able to localize both the evoked and induced activities, and focus source localization on a specific frequency band. Finally, the beamformer offers a spatially filtered estimate of brain activity for each location independently rather than trying to solve the inverse problem by fitting a model to the measured data.

This chapter describes the dynamic causal modeling (DCM) equations, demonstrates how the ensuing model is inverted using Bayesian techniques, and reports the use of Bayesian priors to derive better magnetoencephalography/electoencephalography (EEG) models. It discusses the current DCM algorithms and some promising future developments, and explores the EEG data acquired under a mismatch negativity paradigm. The three plausible models defined under a given architecture and dynamics are examined. The chapter shows that evoked responses, due to bilateral sensory input (e.g., visually or auditory), could be analyzed using DCMs with symmetry priors.

This chapter describes the phase synchrony analysis in electoencephalography or magnetoencephalography data. It defines synchronization generally to be the adjustment of rhythms of self-sustained oscillating systems caused by their interaction. The chapter deals with the most-used techniques for assessing synchronization among brain regions which are distant from one another, and suggests that long-distance synchronization of human brain rhythms serves to functionally integrate distant neural populations into large-scale networks. The analysis of phase locking specializes functional connectivity to a particular form which is independent of oscillatory amplitude, namely that of oscillatory synchronization.

This chapter describes how the signal-to-noise ratio of event-related data can be optimized via experimental design considerations, and examines a variety of strategies to optimize the signal-to-noise ratio of event-related potentials, while keeping the length of the experiment down to a reasonable time. It shows that signal quality can be optimized by careful online monitoring of the incoming electoencephalography (EEG) signals during recording, and knowing when and how to fix faulty EEG channels. During offline analysis, several methods are available that can help in removing large recording artifacts from the data.