Jena, Germany, 1924: Working in near isolation and with meticulous boredom, the psychiatrist Hans Berger observes rhythmic electrical activity of the scalps of human subjects. He is convinced that the activity originates in the brain and introduces the term ‘electroencephalogram’.
It took a decade for the scientific community to accept Berger’s work, which gave birth to the field of electroencephalography, or EEG for short.
Today, the electroencephalogram – also abbreviated as EEG – is commonly known as a medical test that measures the electrical activity of the brain and is used in patients who have or are suspected of having neurological disorders. The EEG provides a window into the living brain, with a continuous electrical readout of what’s happening in our heads. The procedure can be short, often only a 30-minute recording. But for patients being monitored for the diagnosis or treatment of a brain disease, this may continue for much longer – days or even weeks.
As a neurologist specialized in epilepsy, I use EEG every day. Our team at the University of Florida interprets thousands of EEGs per year in neurological patients. I also run a research lab where our goal is to understand the basic structure of the EEG in health and disease.
A history of unexpected twists
The story of EEG is colorful and littered with fables. Berger’s interest in brain electricity was not to fight disease, although that was his day job as a doctor, but to find a biological basis for his belief in telepathy. He wondered if EEG brain waves could transmit thoughts through space, allowing people to read each other’s minds. He was unsuccessful in his mission, but the field he founded took off nonetheless.
By the mid-1930s, researchers had observed the striking differences between the awake and sleeping EEG. The EEGs of patients with brain disease yielded a variety of unprecedented observations.
And then came a defining moment for modern medicine. In December 1934, a group of Boston physicians observed the rhythmic EEG spike wave phenomenon of seizures in patients with “petit mal” epilepsy. Petit mal is an anachronistic term for a form of epilepsy in which a patient’s flow of thought, speech, or action temporarily freezes during seizures. For the first time, patients’ symptoms and behavior during attacks were correlated with a brain signal that occurred in lockstep.
EEG quickly evolved from a scientific curiosity to a mainstream clinical tool. The first clinical EEG laboratory was established at Massachusetts General Hospital in 1937. The practice grew in the following decades to include the specialized services that institutions like ours have been offering since the 1970s.
The EEG explained
What exactly is the EEG?
Imagine taking two small metal disks connected by a conductive wire. Place one disk on the scalp and connect the other to a neutral reference point, such as the ear. Watch as a small alternating current flows through the wire, proportional to the electrical activity sensed by the conductive contact. This activity is the EEG, the electrical environment that bathes brain tissue.
In turn, the EEG arises from the excitable nature of nerve cells or neurons. When neurons fire, action potentials—short, high-voltage spikes that travel outward from their cell bodies—cause local electrical activity in other neurons, causing current to flow within and outside these neurons.
These local current flows can in turn cause the targeted neurons to fire, causing even more current flows. This is how the system maintains itself. The average total activity is a mix of many different frequencies, the five main ones being called delta, theta, alpha, beta and gamma waves.
If the EEG were just a random up-and-down motion—”the bloodless dance of action potentials,” according to a skeptical early 20th century neurologist—it would be much less interesting. The remarkable fact is that EEG tends to spontaneously organize itself into patterns in time and space.
The petit mal spike wave pattern, referred to earlier, is a classic example, but numerous others are now known. Clinical EEG practice is merely recognizing characteristic EEG patterns and correlating them with specific disease states.
Fluctuating neurons
Outside the clinic, a troubling scientific question arises. Simply put: how do electrical patterns arise in the brain? How do the billions of neurons and their trillions of local current flows fluctuate in just the right way to create a globally ordered structure?
Our research group is interested in the fundamental question of pattern formation in EEG. It turns out that activity in the brain is repetitive in nature, that is, oscillatory. This is due to the way neurons are connected and the fact that they interact through excitation and inhibition, creating push-pull effects.
By considering local oscillations as fundamental building blocks, we showed that the EEG over the entire brain could be constructed from such elementary blocks. More interestingly, the different frequencies can be fused or synchronized into a common rhythm. We recognized that this type of synchronization underlies some seizure-like patterns observed in patients.
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EEG, AI and the mind
Pattern formation in nature is very fascinating. How does a leopard get its spots? How does the audience at a concert spontaneously produce rhythmic applause? Many such questions have their origins in a classic article on biological patterns, published in 1952. Its author was Alan Turing, better known as the father of computer science and the early proponent of artificial intelligence, or AI.
The hardware underlying most contemporary AI systems are neural networks. Neural networks were introduced in 1943 by Warren McCulloch, a physician and electroencephalographer. Like Berger, McCulloch’s interest in EEG extended beyond brain diseases. He wondered where in the brain’s neurons and EEG the ability to think lay. He came up with the idea of grouping artificial neuron-like computing units into networks, analogous to how real neurons in the brain are connected.
Together with Walter Pitts, he proved that such neural networks could function as a general-purpose computer. McCulloch-Pitts’ groundbreaking ideas were refined in subsequent decades and reside in the deep learning neural networks of today’s AI.
Deep learning AI has infiltrated all areas of biomedicine, including neurology. For example, AI systems can successfully interpret brain scans. AI methods have also been used to analyze EEG.
Can AI systems be trained to infer thoughts from the EEG? Can AI approach Berger’s quest for telepathy? Incredibly, recent in-depth AI research has shown that some aspects of mental activity can be decoded from EEG.
In 2024, EEG will turn 100. What windows will it open in the brain and mind in the future? Undoubtedly, clinical applications will grow. Brain pattern generation will certainly be better understood. Perhaps EEG will provide a glimpse into the contents of the mind. And for neurologists like me who are charting the AI revolution, there is a quiet pride that EEG really was at the beginning of it all.
This article is republished from The Conversation, an independent nonprofit organization providing facts and analysis to help you understand our complex world.
It is written by: Giridhar Kalamangalam, University of Florida.
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Giridhar Kalamangalam does not work for, consult with, own shares in, or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
