Cover

Half-title

Title

Copyright

Dedication

Contents

Contributors

Preface

1 From neuronal activity to scalp potential fields

Introduction

Spikes and local field potentials

Sinks and sources

Equivalent current dipoles

Spatial smoothness

Oscillations in brain networks

Slow, delta rhythms

Theta rhythms

Alpha rhythms

Spindle (sigma) rhythms

Faster (beta, gamma) rhythms

Conclusions

References

2 Scalp field maps and their characterization

Generic form of scalp field data

Display of a scalp field map

Interpolation

The reference electrode

Spatial derivatives: gradients, current source density and spatial deblurring

Map descriptors

Map descriptors of the spatial distribution of scalp fields: extreme potential values, centroids, electric gravity center

Extreme potential values

Centroids of positive and negative potential areas

Electric gravity center

Advantages and limitations of map descriptors

Map descriptor of the global strength of scalp fields: Global Field Power

Difference maps, amplitude normalization and dissimilarity

Difference maps and pre-stimulus baseline

Normalization and map dissimilarity

References

3 Imaging the electric neuronal generators of EEG/MEG

Introduction

Origin of scalp electric potential differences

Some first steps in estimating cortical activity: from single waves to scalp topography to intracranial dipoles

Imaging the electric neuronal generators with discrete, 3D distributed, linear tomographies

Statistical standardization

The regularization parameter

Adaptive spatial filter for bioelectromagnetic source imaging

Definitions

Source reconstruction using spatial filter

Nonadaptive spatial filter

Adaptive spatial filter

Minimum-variance spatial filter with the unit-gain constraint

Minimum-variance spatial filter with the array gain constraint

Prerequisite for the adaptive spatial filter formulation

Scalar minimum-variance spatial filter

Examples of the source reconstruction

Head volume conductor model and solution space

Conclusion

References

4 Data acquisition and pre-processing standards for electrical neuroimaging

Introduction

Spatial sampling

Distribution of the electrodes

Measurement of the electrode positions

Spatial normalization and interpolation

Artifact detection and elimination

Conclusions

References

5 Overview of analytical approaches

The general model

Temporal elements (model waveforms)

Delta elements

Sinewave elements

Wavelets and Gabor functions

Spatial elements (model topographies)

Single channels

Spatial factor analysis (PCA, ICA, PLS)

Spatial clusters

Topographic component recognition (spatial filters)

Distributed inverse solutions

Common properties of dictionaries and topographies

Combinatorics of data-analytical approaches

Single channel maps and delta-element dictionaries (waveshape analysis)

Single channel maps and sinusoidal dictionaries (power maps and coherence)

Single channel maps and wavelets

Spatial factors and delta-function dictionaries

Spatial factors and sinusoidal dictionaries

Spatial factors and wavelets

Spatial clusters and delta functions (microstates)

Spatial clusters and sinusoidal dictionaries

Spatial clusters and wavelets

Topographic component recognition and delta functions

Distributed inverse solutions in time and frequency

Conclusions

References

6 Electrical neuroimaging in the time domain

Spatial analysis of the spontaneous EEG

Resting state and neurocognitive networks

Functional microstates of the brain

Methods used to analyze microstates of the spontaneous EEG

Spatial analysis of evoked potentials

Multichannel waveform analysis

Analysis of field strength and topography

Microstate analysis

Source analysis

Applications of spatial ERP analysis methods

State-dependent information processing and pre-stimulus baseline

Spatial single-trial ERP analysis

Spatio-temporal analysis of interictal epileptic activity

Conclusion

References

7 Multichannel frequency and time-frequency analysis

Introduction and overview

Frequency-domain EEG analysis

Frequency, amplitude, power and phase of a single channel

Amplitude, power and phase of a single intracerebral oscillating source

Amplitude, power and phase of several intracerebral oscillating sources

Amplitude and power of real EEG data

Phase of real EEG data and measures of phase synchronization

Averaging of complex frequency-domain EEG data

Frequency-domain source models (single phase and multiple phase)

Time-varying oscillations (wavelets)

Relationship to frequency-domain analysis

Display of single-channel time-frequency data

Time-frequency analysis under different models of event-related brain activity

Envelope and phase are event-locked

Envelope is event-locked, but phase is random

Envelope is random, but phase is event-locked

Envelope and phase are random

Reduction of dimensions in frequency- and time-frequency-domain EEG data

Reduction of dimensions in space based on map learning

Reduction of dictionary size by dictionary learning

Conclusions

References

8 Statistical analysis of multichannel scalp field data

Introduction

Basic principles of randomization statistics and a “toy” example

Applications for scalp field data

The sample data

Overview of the analyses

Testing the topography consistency across subjects

Comparison of map differences between conditions

Test for the frequency and duration of effects of the design

Post-hoc tests

Partial least squares

Test for correlations with language proficiency increase

Randomization tests on microstate assignment

Concluding remarks

References

9 State space representation and global descriptors of brain electrical activity

The state space representation

The notion of state space

Structure of the EEG state space

Vector space structure

Metric structure

Relation to topographical analysis

Linear transformations

Projections

Principal component analysis

Principal components

Phenomenology of EEG trajectories

Source model and spatial modes

Global descriptors

Principle of dimensional simplicity

Integral field strength and generalized frequency

Measure of spatial complexity

Three-dimensional representation of global functional states

Transformations in the - space

Extensions and modifications

Regional measures

Local complexity differentials

Omega complexity as a function of frequency

Omega complexity production rate

Omega complexity from correlation matrices

Record-wise variance normalization

Selected applications

Sleep stages and vigilance variations

Epilepsy and paroxysmal brain activity

Neuropathology, neuropsychiatry

Effects of neuroactive substances

Developmental changes of EEG

Sensory and motor processes

Miscellaneous topics

Relations to other analytical approaches

Microstate models

Synchronization measures

Summary

Acknowledgements

List of symbols

References

10 Integration of electrical neuroimaging with other functional imaging methods

Introduction

Combining EEG and MEG

Combining EEG with fMRI

Combining EEG with fMRI using sequential recordings

Combining EEG with fMRI in developmental populations

Combining EEG with fMRI in simultaneous recordings

Simultaneous EEG–fMRI in epilepsy

Simultaneous EEG-fMRI in resting state recordings

Further simultaneous EEG–fMRI applications

The EEG and transcranial magnetic stimulation

Other combinations and conclusions

References

Index