Jena, Germany, 1924: Working almost in isolation and with great care, psychiatrist Hans Berger observes rhythmic electrical activity in the scalp of human subjects. He believes that the activity originates in the brain and uses the term “electroencephalogram”.
It would take 10 years for Berger’s work, which gave birth to the field of electroencephalography, or EEG for short, to be accepted by the scientific community.
Today, the electroencephalogram, also abbreviated as EEG, is widely known as a medical test that measures brain electrical activity, used in patients with or suspected of having neurological disorders. EEG provides a window into the living brain, providing a continuous electrical readout of what’s going on inside our heads. The procedure can be short, usually just a 30-minute recording. However, for patients being monitored for diagnosis or treatment of brain disease, this treatment can be continued for a much longer period of time (days or even weeks).
As a neurologist specializing in epilepsy, I use EEG on a daily basis. Our team at the University of Florida interprets thousands of EEGs per year in neurological patients. I also run a research laboratory where our goal is to understand the fundamental structure of the EEG in health and disease.
A story of unexpected developments
The story of EEG is colorful and full of tales. Berger’s interest in brain electricity was not to fight diseases, although this was his day job as a doctor, but to find a biological basis for his belief in telepathy. He wondered whether EEG brain waves could carry thoughts through space and allow people to read each other’s minds. He failed in his mission, but the field he founded still rose.
By the mid-1930s, researchers observed striking differences between the awake and sleeping EEG. EEGs of patients with brain disease have revealed a variety of unprecedented observations.
And then came a decisive moment for modern medicine. In December 1934, a group of Boston physicians observed the rhythmic EEG spike-wave 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 momentarily freezes during a seizure. For the first time, patients’ symptoms and behavior during seizures were linked to a simultaneously occurring brain signal.
EEG has rapidly evolved from a scientific curiosity to a mainstream clinical tool. The first clinical EEG laboratory was established at Massachusetts General Hospital in 1937. In subsequent years, the practice evolved into the specialized services that institutions like ours have been offering since the 1970s.
EEG explained
So what exactly is EEG?
Imagine taking two small metal discs connected by a conductive wire. Place one of the disks on the scalp and connect the other to a neutral reference such as the ear. Monitor the small alternating current in the wire proportional to the electrical activity detected by the conductive contact. This activity is EEG, the electrical medium that bathes brain tissue.
In contrast, EEG arises from the excitable nature of nerve cells or neurons. When neurons fire, action potentials (short, high-voltage spikes that travel outward from cell bodies) generate local electrical activity in other neurons, causing current to flow in and out of them.
These local current flows can cause targeted neurons to fire in turn and generate further current flows. Thus, the system sustains itself. Average overall activity is a mixture of many different frequencies, with five main frequencies called delta, theta, alpha, beta and gamma waves.
If the EEG were just random drifting up and down—”a bloodless dance of action potentials,” as one skeptical early 20th-century neurologist commented—it would be much less interesting. The remarkable fact is that EEG tends to self-organize in time and space.
The pointed wave pattern of the petit mal mentioned earlier is a classic example, but numerous other examples are now known. Clinical EEG application is simply recognizing characteristic EEG patterns and relating them to specific disease states.
fluctuating neurons
Beyond the clinic, a disturbing scientific question arises. Simply put, how do electrical patterns in the brain emerge? How do billions of neurons and trillions of local current flows fluctuate in the right direction to form a globally ordered structure?
Our research group is interested in the fundamental question of pattern formation in EEG. It turns out that the activity in the brain is naturally repetitive, that is, oscillatory. This is because of the way neurons are connected and interact through excitation and inhibition to produce push-pull effects.
By treating local oscillations as basic building blocks, we show that the entire brain EEG can be constructed from such basic blocks. More interestingly, different frequencies can be combined or synchronized into a common rhythm. We noticed that this type of synchronization underlies some of the seizure-like patterns observed in patients.
EEG, artificial intelligence and the mind
Pattern formation in nature is extremely fascinating. How does a leopard get its spots? How does the audience at a concert become accustomed to spontaneous rhythmic applause? The origins of many such questions date back to a classic paper on biological patterning published in 1952. Its author was Alan Turing, better known as the father of computer science and the first proponent of artificial intelligence or artificial intelligence.
The hardware underlying most of today’s artificial intelligence 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 disease. He wondered where the thinking capacity of the neurons and EEG in the brain were located. He conceived the idea of grouping artificial neuron-like computing units into networks, similar to how real neurons in the brain are wired together.
Together with Walter Pitts, he proved that such neural networks could work as a general-purpose computer. The seminal McCulloch-Pitts ideas were developed over the following decades and incorporated into the “deep learning” neural networks of today’s artificial intelligence.
Deep learning AI has infiltrated all areas of biomedicine, including neurology. For example, artificial intelligence systems can successfully interpret brain scans. Artificial intelligence methods were also used to analyze EEG.
Can AI systems be trained to extract thoughts from EEG? Could artificial intelligence come close to Berger’s quest for telepathy? Incredibly, recent deep learning AI research has shown that some aspects of mental activity can be deciphered from EEG.
In 2024, EEG will be 100 years old. What kind of windows will it open to the brain and mind in the future? Undoubtedly, clinical applications will increase. Of course, brain model generation will be better understood. Maybe EEG can take a look at the contents of the mind. And for neuroscientists like me who study the AI revolution, there’s a quiet pride in the fact that the EEG was actually at the beginning of it all.
This article is republished from The Conversation, an independent, nonprofit news organization providing facts and analysis to help you understand our complex world.
Written by: Giridhar Kalamangalam, university of florida.
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Giridhar Kalamangalam does not work for, consult, own shares in, or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations other than academic appointments.