Where Does EEG Come From and What Does It Mean?

EEG is one of the most important non-invasive brain imaging tools in neuroscience and in the clinic, but surprisingly little is known about how activity in neural circuits produces the various EEG features linked to cognition. The ‘standard model’ of EEG states that simultaneous postsynaptic potentials of neural populations produces EEG, but this explains only the existence of EEG, not the meaning of the content of the EEG signal. No ‘grand unified theories’ are presented, because there is unlikely to be a single ‘neural correlate of EEG’. More experiments, analyses, and models that span multiple spatial scales are necessary. Recent advances in neuroscience knowledge and technologies make this an ideal time for new discoveries about the origins and significances of EEG. This research will benefit fundamental neuroscience, cognitive neuroscience, clinical diagnoses, and data analysis development. Electroencephalography (EEG) has been instrumental in making discoveries about cognition, brain function, and dysfunction. However, where do EEG signals come from and what do they mean? The purpose of this paper is to argue that we know shockingly little about the answer to this question, to highlight what we do know, how important the answers are, and how modern neuroscience technologies that allow us to measure and manipulate neural circuits with high spatiotemporal accuracy might finally bring us some answers. Neural oscillations are perhaps the best feature of EEG to use as anchors because oscillations are observed and are studied at multiple spatiotemporal scales of the brain, in multiple species, and are widely implicated in cognition and in neural computations. Electroencephalography (EEG) has been instrumental in making discoveries about cognition, brain function, and dysfunction. However, where do EEG signals come from and what do they mean? The purpose of this paper is to argue that we know shockingly little about the answer to this question, to highlight what we do know, how important the answers are, and how modern neuroscience technologies that allow us to measure and manipulate neural circuits with high spatiotemporal accuracy might finally bring us some answers. Neural oscillations are perhaps the best feature of EEG to use as anchors because oscillations are observed and are studied at multiple spatiotemporal scales of the brain, in multiple species, and are widely implicated in cognition and in neural computations. 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, who was trying to discover a ‘mechanism’ for extra-sensory phenomena. It was known since the late 19th century that the brain produces electrical fields, and that these fields exhibit oscillations; Berger’s great contributions included demonstrating that these fields could be measured in humans from outside the brain, and demonstrating that neural oscillations were related to cognitive phenomena such as sensory processing and solving mathematical equations. this term is used here as shorthand for an idiosyncratic spatial/temporal/spectral pattern that is associated with a particular sensory or cognitive process, similar to a ‘fingerprint’ [17]. Examples include midfrontal theta and response conflict monitoring, and posterior alpha power and spatial attention. brain function can be measured at many spatial scales, ranging from individual synapses (∼10 nm) to whole-brain networks (∼10 cm). Although neuroscience research in general spans all these spatial scales, there is little understanding of how the dynamics are related across spatial scales. Is understanding multiscale dynamics important for understanding brain function? No-one really knows, but multiscale dynamics are hypothesized to be necessary for the complexity required for higher cognitive functioning including consciousness [93]. Studying multiscale interactions presents conceptual, mathematical, and technological challenges, and scientists tend to like challenges. a microcircuit refers to a spatial scale of brain anatomical/functional organization that is larger than a single neuron but smaller than an fMRI voxel. Microcircuits can take several forms; the term ‘microcircuit’ often connotes a bundling of dozens or hundreds of cells of various classes that are more densely interconnected than they are connected to neighboring microcircuits, and that work together towards a common function [57]. Orientation-tuned columns in primate V1 is an example of a microcircuit. the EEG activity of a living brain is not flat, nor is it random. Instead, EEG is dominated by rhythms that are grouped into a small number of characteristic frequencies. These rhythms are driven by fluctuations in excitability of populations of neurons, and have complex spatiotemporal patterns that vary in amplitude, timing, and frequency. These variations are known as nonstationarities, and the general goal of cognitive electrophysiology is to understand how and why these nonstationarities are related to various cognitive and perceptual processes.