by Jena, Germany, 1924: Working in near-solitude and with alacrity, psychiatrist Hans Berger observes rhythmic electrical activity from people’s scalps. It is certain that the activity originates from within the brain and the basis of the term “electroencephalogram.”
It is 10 years before the scientific community accepts Berger’s work, giving rise to the field of electroencephalography, or EEG for short.
Today, the electroencephalogram – also known as EEG – is widely known as a medical test that measures the brain’s electrical activity used on patients with, or suspected of, neurological disorders. The EEG provides a window into the living brain, with a continuous electrical readout of what is happening inside our heads. The procedure can be short, often recording only 30 minutes. But for patients who are monitored for diagnosis or treatment of brain disease, it can continue for much longer – days or even weeks.
As a neurologist specializing in epilepsy, I use EEG daily. Our team at the University of Florida interprets thousands of EEGs per year in neurological patients. I also run a research laboratory where we aim to understand the basic structure of the EEG in health and disease.
A history of unexpected twists
The story of EEG is colorful and full of legends. Berger’s interest in brain electricity was not to fight disease, although that was his day job as a physician, but to find a biological basis for his belief in teleopathy. He wondered if EEG brain waves could communicate thoughts through space, allowing people to read each other’s minds. He did not succeed in his mission, but nevertheless the field he founded progressed.
By the mid-1930s, researchers had noticed the significant differences between waking 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 spike-wave EEG appearance of seizures in patients with “petit mal” epilepsy. Petit mal is an anachronistic term for a type of epilepsy in which the patient’s flow of thought, speech or action is restricted during seizures. For the first time, a patient’s symptoms and behavior during seizures 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 over the following years into the specialized services offered by institutions like ours since the 1970s.
The EEG explained
So what, exactly, is the EEG?
Imagine taking two small metallic discs connected by a conducting wire. Place one disc on the scalp and attach the other to a neutral reference, such as the ear. Observe the flow of a tiny alternating current in the wire, proportional to the electrical activity sensed by the conducting contact. This activity is the EEG, the type of electricity that floats through 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 traveling out of their cell bodies—trigger local electrical activity in other neurons, causing current to flow in and out of them.
These local current flows may in turn fire the targeted neurons and set up more current flows. Thus, the system sustains itself. The overall average activity is a mixture of 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 stream – “a bloodless dance of action potentials,” said a skeptical 20th-century neuroscientist – it would be much less interesting. The remarkable fact is that EEG tends to spontaneously organize into patterns in time and space.
The spike-wave pattern of petit mal, referred to earlier, is a classic example, but scores of others are now known. The clinical practice of EEG involves simply identifying characteristic EEG patterns and correlating them with specific disease states.
Volatile neurons
Outside the clinic, a disturbing scientific question arises. Simply put, how do electrical patterns arise in the brain? How do billions of neurons and their trillions of local current flows change in the right ways to create global structure?
Our research group was interested in the basic question of pattern formation in EEG. It turns out that activity in the brain is naturally repetitive – that is, oscillatory. This is due to the way neurons are connected and the fact that they interact through excitation and inhibition to produce push-pull effects.
Considering local oscillations as basic building blocks, we showed that the EEG over the whole brain could be built from such building blocks. More interesting than that, the different frequencies could be made to merge, or synchronize. We identified that this type of synchronization underlies some seizure patterns observed in patients.
EEG, AI and the mind
Pattern formation in nature is fascinating. How does a leopard find its spots? How does the audience at a concert spontaneously decide to clap rhythmically? Many such questions trace their origins to a classic paper on biological patterning published in 1952. The author was Alan Turing, better known as the father of computer science and an early advocate of artificial intelligence, or AI.
The hardware underlying most AI systems today are neural networks. Warren McCulloch, a physician and electroencephalographer, introduced neural networks in 1943. Like Berger, McCulloch’s interest in EEG went beyond brain disease. I wonder where in the neurons of the brain and EEG where the ability to think was. He came up with the idea of grouping neural artificial computing units into networks, similar to how real neurons in the brain were interconnected.
Together with Walter Pitts, he proved that such neural networks could act as a multipurpose computer. McCulloch-Pitts’ brilliant ideas were refined over the following years and are in today’s AI “deep learning” neural networks.
Deep learning AI has infiltrated all areas of biomedicine, including neuroscience. For example, AI systems can successfully interpret brain scans. AI methods have also been used to analyze EEG.
Can AI systems be trained to extract ideas from the EEG? Can AI take on Berger in his quest for telepathy? Amazingly, recent deep learning AI research has shown that certain aspects of mental activity could be decoded from EEG.
In 2024, EEG turns 100. What windows will it open into the brain and mind in the future? Undoubtedly, clinical applications will grow. Certainly, there will be a better understanding of brain pattern generation. Perhaps an EEG will provide insight into the content of the mind. And for neuroscientists like me who survey the AI revolution, there is a quiet pride that EEG really was at the beginning of life.
This article is republished from The Conversation, a non-profit, independent news organization that brings you facts and analysis to help you make sense of our complex world.
It was 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 this, and has not disclosed any relevant connections beyond their academic appointment.
