Elsevier

NeuroImage

Volume 61, Issue 2, June 2012, Pages 371-385
NeuroImage

Review
Towards the utilization of EEG as a brain imaging tool

https://doi.org/10.1016/j.neuroimage.2011.12.039Get rights and content

Abstract

Recent advances in signal analysis have engendered EEG with the status of a true brain mapping and brain imaging method capable of providing spatio-temporal information regarding brain (dys)function. Because of the increasing interest in the temporal dynamics of brain networks, and because of the straightforward compatibility of the EEG with other brain imaging techniques, EEG is increasingly used in the neuroimaging community. However, the full capability of EEG is highly underestimated. Many combined EEG-fMRI studies use the EEG only as a spike-counter or an oscilloscope. Many cognitive and clinical EEG studies use the EEG still in its traditional way and analyze grapho-elements at certain electrodes and latencies. We here show that this way of using the EEG is not only dangerous because it leads to misinterpretations, but it is also largely ignoring the spatial aspects of the signals. In fact, EEG primarily measures the electric potential field at the scalp surface in the same way as MEG measures the magnetic field. By properly sampling and correctly analyzing this electric field, EEG can provide reliable information about the neuronal activity in the brain and the temporal dynamics of this activity in the millisecond range. This review explains some of these analysis methods and illustrates their potential in clinical and experimental applications.

Highlights

► EEG is a cost-effective, easy-to-use brain imaging method. ► EEG spatial analyses render unambiguous neurophysiologic interpretability. ► EEG source imaging can be readily applied in clinical and experimental settings.

Introduction

Over 80 years ago the EEG was first described with the promise of it providing a “window into the brain” (Berger, 1929). However, the transparency of this window has been obscured in the sense that the sources in the brain that produced the signals on the scalp were not readily visible. Recent advances in EEG recording technology and EEG analysis methods made this window much more transparent, and the signal–source relationship has become clearer. In this review we overview some of the basic methods that render EEG a comprehensive and powerful brain-imaging tool that directly maps the brain neuronal activity with reasonable spatial and superb temporal resolution.

During the largest part of the 80 years of existence of EEG, and unfortunately to some extent still today, the analytic potential of EEG has not been fully exploited. On the contrary, several serious misunderstandings about the generation of the scalp potentials have led to wrong interpretations of the data and to claims about brain functions that were later falsified by intracranial recordings, lesion studies, or neuroimaging methods; thereby severely discrediting EEG.

Such misinterpretations were mainly due to the ignorance of important physical principles that underlie the measurement of electric potentials at the scalp surface. Most important is the fact that a given electrode on the scalp does not record solely the neuronal activity directly underlying it. Rather, every electrode picks up signals from different sources that can eventually be quite distal. This is because the electric field of each active source in the brain spreads in all directions and is thus picked up to a variable extent by each electrode. This also holds for the reference electrode against which the potential at one scalp electrode is compared. Fluctuation of the voltage at the reference electrode will lead to changes of the potential at the active electrode even if the voltage at that point was actually stable. There is no point that is electrically silent and could be considered as true zero potential. Thus, changing the reference position will change the absolute potential at the active electrode because EEG forcibly entails recording potential differences. This reference-dependent feature of EEG potentials is often cited as a major drawback of EEG as compared for example to MEG (Hari, 2011).

However, it is important to note and to insist on the fact that the topography of the potential field is completely independent of the choice of the reference (Geselowitz, 1998). Because it is the topography of the electric or magnetic field that is the only relevant information used for electric or magnetic source imaging, the so-called “reference-problem” of the EEG effectively does not exist and a search for an optimal reference (Gencer et al., 1996) for source imaging is meaningless.

In this article we overview EEG analysis methods that are based on the understanding of the biophysical principles that lead to the potential field on the scalp and that are quantifying the properties of this potential field in time and in space. Analysis methods that are based on single channel waveforms are not considered here, because such analyses are ambiguous with respect to 1) the underlying generators as well as more general neurophysiologic causes and 2) the statistical confidence that can be placed on them (Michel et al., 2009, Murray et al., 2008, Murray et al., 2009).

Section snippets

Principles of EEG spatial analysis

The EEG is traditionally analyzed in terms of temporal waveforms at certain channels, looking at power of rhythms in the spontaneous EEG, at amplitude and latency of the peaks and troughs in event-related potentials (ERPs), or at particular grapho-elements in pathological or sleep stages. There is no doubt that this type of analysis has provided many important insights regarding brain functioning in health and disease, but it has not been considered as an imaging method in the sense that one

Spatial analysis of the EEG at rest

In recent years the term “Resting State” has been established and has received considerable attention in the brain imaging literature (Fox and Raichle, 2007). It mainly refers to the coherent fluctuations of blood oxygen level dependent (BOLD) responses in different brain regions measured with fMRI, while the subject is at rest without any particular stimulus or task. Using independent component analysis, distinct patterns of coherent activity in large-scale networks have been identified,

Spatial analysis of multichannel stimulus-related activity

Aside from the analysis of spontaneous EEG, there is also a considerable body of research that focuses on stimulus-related brain activity with a particular interest in how and when the brain processes specific types of information and/or generates decisions or actions in response to external stimuli in conjunction with mental operations. This line of enquiry into mental chronometry stems in large part from the pioneering efforts of Donders and later Sternberg (reviewed in Vaughan, 1990). Their

Clinical applications of spatial EEG analysis

The spatial analysis methods of multichannel EEG described in this review have been applied to a wide range of clinical populations, and we review only a selected sampling here.

Conclusions and outlook

The aim of this review was to illustrate the paradigm change that took place with respect to the EEG analysis in the last decade or so. EEG analysis moved away from the traditional analysis of grapho-elements at certain electrodes to a comprehensive analysis of the brain's electric field at the scalp. This movement was certainly largely inspired by MEG work, which from the onset rather analyzed the spatial than the temporal patterns. We tried to show here that spatial EEG analysis is not solely

Acknowledgments

The authors receive support from the Swiss National Science Foundation (grants 310030-132952 and 33CM30-124089 to CMM, grants 310030B-133136, K-33K1_122518, and 320030_120579 to MMM as well as the NCCR SYNAPSY grant to CMM and MMM).

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