How many configurations can produce a single EEG
Trends in Neurosciences
OpinionWhere Does EEG Come From and What Does It Mean?
Section snippets
What Is the Answer to the Title Question?
When the question in the title of this paper is posed to colleagues, textbooks, or the Internet, the answers often involve some combination of a description of Maxwell’s equations regarding volume conduction of electrical potentials and mathematical descriptions of anatomical localization algorithms 1, 2. The assumption behind this answer is that understanding the significance of EEG requires solving the ill-posed inverse problem: given an observed topographical distribution of voltage values,
The Ultimate but Ultimately Unattainable Goal: One-to-One Mapping between EEG Feature and Microcircuit Configuration
EEG could be a much more powerful and insightful brain measurement tool if only we could identify one-to-one mappings between EEG feature and neural microcircuit configuration, as in Figure 1C. In truth, the relationship between EEG feature and microcircuit configuration is likely to be at best ‘few-to-some’, meaning that a small number of EEG features may correspond to a larger (but hopefully not very large) number of microcircuit configurations. In part, this is necessarily true because EEG
EEG Oscillations Are an Excellent Link to Neurophysiology
Neural oscillations are the most prominent feature of EEG data, and countless studies over many decades have demonstrated that perceptual, cognitive, motor, and emotional processes are tightly linked with specific patterns of oscillations [17]. Oscillations are observed throughout the nervous system and at multiple spatial and temporal scales [18], and they seem to be ubiquitous across species [19]. Taken together, this suggests that oscillations have important functions that have been
What Then Do We Know about Where EEG Comes From?
From a biophysics perspective, much is known about the origins of the local field potential (LFP) and EEG [23]. The (here termed) ‘standard model’ states that LFP and EEG are the extracellular currents reflecting summed dendritic postsynaptic potentials (the exchange of electrochemical signaling across the synapse) in thousands to millions of pyramidal cells in parallel alignment 2, 24, 25. Although postsynaptic potentials make the largest contributions, other neural processes including calcium
Now Is the Time To Start Answering the Title Question
We are at the convergence of three developments that together provide the opportunity for new and important discoveries about the origins and significances of EEG features. The first development is the bulging of the literature that characterizes EEG features accompanying memory, perception, emotion, language, action, and other cognitive processes. One can criticize such investigations as being correlational, too macroscopic, or unable to determine whether oscillations are part of the
What Types of Data Are Needed?
To discover what EEG means, insightful experiments are likely to include empirical measurements of neural data across multiple spatial scales recorded simultaneously. The ideal dataset includes several sets of laminar probes comprising hundreds of microelectrodes that span multiple layers of the cortex and multiple cortical regions, in combination with EEG on the skull or scalp. The EEG should have sufficient density (ideally >30 electrodes) to create topographical maps and implement spatial
What Types of Analyses Are Needed?
Properly analyzing the types of datasets described above may require novel, or at least different, analyses compared to the standard corpus of EEG analysis techniques (Figure 2). For one thing, the majority of time–frequency methods assume sinusoidal activity at the timescale of hundreds of milliseconds [80]. Clearly, neural oscillations are (by definition) rhythmic, but are they sinusoidal? That is less clear. There are noteworthy cases of nonsinusoidal neural oscillations, including up–down
Anticipated Challenges
Although it is not fashionable in neuroscience to highlight inter-species differences, there may be significant difficulties when trying to generalize findings across, for example, rodents and humans. Some of these difficulties may be relatively tractable. For example, differences in skull thickness and electrode size mean that a scalp electrode in humans measures activity from a larger neural population than does a screw drilled into a mouse skull. Other species differences may be more
Why This Research Is Important
Despite (or perhaps because of) these difficulties, this type of research is important and must be done. The benefits to fundamental neuroscientific knowledge, ideas about the roles of multiscale integration in cognition 18, 93, and clinical diagnosis are myriad, including the categories listed below.
Concluding Remarks
The literature linking human EEG oscillations to cognition is large and growing rapidly. It is imperative to work towards an understanding of the neurophysiological phenomena that drive those oscillations, and of the implications these oscillations have for how cognitive computations are implemented at the neural level. Linking brain dynamics across spatial and measurement scales is one of the great challenges in 21st century neuroscience.
Acknowledgments
Work in the laboratory of M.X.C. is funded by a grant from the European Research Council (ERC-StG 638589).
Glossary
- Electroencephalography (EEG)
- the measurement of brain electrical fields via electrodes (which act as small antennas) placed on the head. The electrical fields are the result of electrochemical signals passing from one neuron to the next. When billions of these tiny signals are passed simultaneously in spatially extended and geometrically aligned neural populations, the electrical fields sum and become powerful enough to be measured from outside the head. EEG is often attributed to Hans Berger,
References (98)
- et al.
Minimum-norm cortical source estimation in layered head models is robust against skull conductivity error
Neuroimage
(2013) A standardized boundary element method volume conductor model
Clin. Neurophysiol.
(2002)Experimental tests of EEG source localization accuracy in spherical head models
Clin. Neurophysiol.
(2001)- et al.
How to use fMRI functional localizers to improve EEG/MEG source estimation
J. Neurosci. Methods
(2015) Five-dimensional neuroimaging: localization of the time-frequency dynamics of cortical activity
Neuroimage
(2008)High-resolution retinotopic maps estimated with magnetoencephalography
Neuroimage
(2017)Spontaneous events outline the realm of possible sensory responses in neocortical populations
Neuron
(2009)Microstates in resting-state EEG: current status and future directions
Neurosci. Biobehav. Rev.
(2015)- et al.
Physiological plausibility can increase reproducibility in cognitive neuroscience
Trends Cogn. Sci.
(2016) Scaling brain size, keeping timing: evolutionary preservation of brain rhythms
Neuron
(2013)
EEG and MEG: relevance to neuroscience
Neuron
A biophysically detailed model of neocortical local field potentials predicts the critical role of active membrane currents
Neuron
Modeling the spatial reach of the LFP
Neuron
Frequency-band coupling in surface EEG reflects spiking activity in monkey visual cortex
Neuron
Understanding the relationships between spike rate and delta/gamma frequency bands of LFPs and EEGs using a local cortical network model
Neuroimage
EEG alpha oscillations: the inhibition–timing hypothesis
Brain Res. Rev.
Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing
Trends Neurosci.
Perceptual cycles
Trends Cogn. Sci.
Relative contributions of intracortical and thalamo-cortical processes in the generation of alpha rhythms, revealed by partial coherence analysis
Electroencephalogr. Clin. Neurophysiol.
Inter- and intra-individual variability in alpha peak frequency
Neuroimage
Cortical enlightenment: are attentional gamma oscillations driven by ING or PING?
Neuron
Dissecting local circuits in vivo: integrated optogenetic and electrophysiology approaches for exploring inhibitory regulation of cortical activity
J. Physiol. Paris
Rhythms for cognition: communication through coherence
Neuron
Parametric variation of gamma frequency and power with luminance contrast: a comparative study of human MEG and monkey LFP and spike responses
Neuroimage
A neural microcircuit for cognitive conflict detection and signaling
Trends Neurosci.
Which ‘neural activity’ do you mean? fMRI, MEG, oscillations and neurotransmitters
Neuroimage
Distinct roles of the cortical layers of area V1 in figure–ground segregation
Curr. Biol.
Canonical microcircuits for predictive coding
Neuron
Mining event-related brain dynamics
Trends Cogn. Sci.
Discrimination of cortical laminae using MEG
Neuroimage
The θ–γ neural code
Neuron
When brain rhythms aren’t ‘rhythmic’: implication for their mechanisms and meaning
Curr. Opin. Neurobiol.
Topographic mapping of rapid transitions in EEG multiple frequencies: EEG frequency domain of operational synchrony
Neurosci. Res.
Frequency flows and the time-frequency dynamics of multivariate phase synchronization in brain signals
Neuroimage
Mechanism and significance of global coherence in scalp EEG
Curr. Opin. Neurobiol.
Brain oscillations and the importance of waveform shape
Trends Cogn. Sci.
Dynamic causal modelling: a critical review of the biophysical and statistical foundations
Neuroimage
Time-shift denoising source separation
J. Neurosci. Methods
Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition
J. Neurosci. Methods
Cingulate cortex: diverging data from humans and monkeys
Trends Neurosci.
The brainweb of cross-scale interactions
New Ideas Psychol.
High-frequency gamma oscillations and human brain mapping with electrocorticography
Prog. Brain Res.
The neurophysiological bases of EEG and EEG measurement: a review for the rest of us
Psychophysiology
Electric Fields of the Brain: The Neurophysics of EEG
Electromagnetic Brain Imaging: A Bayesian Perspective
Source connectivity analysis with MEG and EEG
Hum. Brain Mapp.
A new approach to neuroimaging with magnetoencephalography
Hum. Brain Mapp.
Advances in spike localization with EEG dipole modeling
Clin. EEG Neurosci.
Dynamic circuit motifs underlying rhythmic gain control, gating and integration
Nat. Neurosci.
Cited by (332)
Electrophysiological correlates of the emotional response on brain activity in adolescents
2024, Biomedical Signal Processing and ControlResting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes
2024, Neurobiology of DiseaseArtificial sharp-wave-ripples to support memory and counter neurodegeneration
2024, Brain ResearchAnalyzing neural activity under prolonged mask usage through EEG
2024, Brain Research