Erika Nyhus, Bowdoin College

Electroencephalography, or EEG, was invented 100 years ago. In the years since the invention of this device to monitor brain electricity, it has had an incredible impact on how scientists study the human brain.

Since its first use, the EEG has shaped researchers' understanding of cognition, from perception to memory. It has also been important for diagnosing and guiding treatment of multiple brain disorders, including epilepsy.

I am a cognitive neuroscientist who uses EEG to study how people remember events from their past. The EEG's 100-year anniversary is an opportunity to reflect on this discovery's significance in neuroscience and medicine.

Discovery of EEG

On July 6, 1924, psychiatrist Hans Berger performed the first EEG recording on a human, a 17-year-old boy undergoing neurosurgery. At the time, Berger and other researchers were performing electrical recordings on the brains of animals.

What set Berger apart was his obsession with finding the physical basis of what he called psychic energy, or mental effort, in people. Through a series of experiments spanning his early career, Berger measured brain volume and temperature to study changes in mental processes such as intellectual work, attention and desire.

He then turned to recording electrical activity. Though he recorded the first traces of EEG in the human brain in 1924, he did not publish the results until 1929. Those five intervening years were a tortuous phase of self-doubt about the source of the EEG signal in the brain and refining the experimental setup. Berger recorded hundreds of EEGs on multiple subjects, including his own children, with both experimental successes and setbacks.

Finally convinced of his results, he published a series of papers in the journal Archiv für Psychiatrie and had hopes of winning a Nobel Prize. Unfortunately, the research community doubted his results, and years passed before anyone else started using EEG in their own research.

Berger was eventually nominated for a Nobel Prize in 1940. But Nobels were not awarded that year in any category due to World War II and Germany's occupation of Norway.

Neural oscillations

When many neurons are active at the same time, they produce an electrical signal strong enough to spread instantaneously through the conductive tissue of the brain, skull and scalp. EEG electrodes placed on the head can record these electrical signals.

Since the discovery of EEG, researchers have shown that neural activity oscillates at specific frequencies. In his initial EEG recordings in 1924, Berger noted the predominance of oscillatory activity that cycled eight to 12 times per second, or 8 to 12 hertz, named alpha oscillations. Since the discovery of alpha rhythms, there have been many attempts to understand how and why neurons oscillate.

Neural oscillations are thought to be important for effective communication between specialized brain regions. For example, theta oscillations that cycle at 4 to 8 hertz are important for communication between brain regions involved in memory encoding and retrieval in animals and humans.

Researchers then examined whether they could alter neural oscillations and therefore affect how neurons talk to each other. Studies have shown that many behavioral and noninvasive methods can alter neural oscillations and lead to changes in cognitive performance. Engaging in specific mental activities can induce neural oscillations in the frequencies those mental activities use. For example, my team's research found that mindfulness meditation can increase theta frequency oscillations and improve memory retrieval.

Noninvasive brain stimulation methods can target frequencies of interest. For example, my team's ongoing research found that brain stimulation at theta frequency can lead to improved memory retrieval.

EEG has also led to major discoveries about how the brain processes information in many other cognitive domains, including how people perceive the world around them, how they focus their attention, how they communicate through language and how they process emotions.

Diagnosing and treating brain disorders

EEG is commonly used today to diagnose sleep disorders and epilepsy and to guide brain disorder treatments.

Scientists are using EEG to see whether memory can be improved with noninvasive brain stimulation. Although the research is still in its infancy, there have been some promising results. For example, one study found that noninvasive brain stimulation at gamma frequency – 25 hertz – improved memory and neurotransmitter transmission in Alzheimer's disease.

A new type of noninvasive brain stimulation called temporal interference uses two high frequencies to cause neural activity equal to the difference between the stimulation frequencies. The high frequencies can better penetrate the brain and reach the targeted area. Researchers recently tested this method in people using 2,000 hertz and 2,005 hertz to send 5 hertz theta frequency at a key brain region for memory, the hippocampus. This led to improvements in remembering the name associated with a face.

Although these results are promising, more research is needed to understand the exact role neural oscillations play in cognition and whether altering them can lead to long-lasting cognitive enhancement.

The future of EEG

The 100-year anniversary of the EEG provides an opportunity to consider what it has taught us about brain function and what this technique can do in the future.

In a survey commissioned by the journal Nature Human Behaviour, over 500 researchers who use EEG in their work were asked to make predictions on the future of the technique. What will be possible in the next 100 years of EEG?

Some researchers, including myself, predict that we'll use EEG to diagnose and create targeted treatments for brain disorders. Others anticipate that an affordable, wearable EEG will be widely used to enhance cognitive function at home or will be seamlessly integrated into virtual reality applications. The possibilities are vast.

Erika Nyhus, Associate Professor of Psychology and Neuroscience, Bowdoin College

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Lynn Greenky, Syracuse University

The U.S. Supreme Court has sent back to lower courts the decision about whether states can block social media companies such as Facebook and X, formerly Twitter, from regulating and controlling what users can post on their platforms.

Laws in Florida and Texas sought to impose restrictions on the internal policies and algorithms of social media platforms in ways that influence which posts will be promoted and spread widely and which will be made less visible or even removed.

In the unanimous decision, issued on July 1, 2024, the high court remanded the two cases, Moody v. NetChoice and NetChoice v. Paxton, to the 11th and 5th U.S. Circuit Courts of Appeals, respectively. The court admonished the lower courts for their failure to consider the full force of the laws' applications. It also warned the lower courts to consider the boundaries imposed by the Constitution against government interference with private speech.

Contrasting views of social media sites

In their arguments before the court in February 2024, the two sides described competing visions of how social media fits into the often overwhelming flood of information that defines modern digital society.

The states said the platforms were mere conduits of communication, or “speech hosts,” similar to legacy telephone companies that were required to carry all calls and prohibited from discriminating against users. The states said that the platforms should have to carry all posts from users without discrimination among them based on what they were saying.

The states argued that the content moderation rules the social media companies imposed were not examples of the platforms themselves speaking – or choosing not to speak. Rather, the states said, the rules affected the platforms' behavior and caused them to censor certain views by allowing them to determine whom to allow to speak on which topics, which is outside First Amendment protections.

By contrast, the social media platforms, represented by NetChoice, a tech industry trade group, argued that the platforms' guidelines about what is acceptable on their sites are protected by the First Amendment's guarantee of speech free from government interference. The companies say their platforms are not public forums that may be subject to government regulation but rather private services that can exercise their own editorial judgment about what does or does not appear on their sites.

They argued that their policies were aspects of their own speech and that they should be allowed to develop and implement guidelines about what is acceptable speech on their platforms based on their own First Amendment rights.

A reframe by the Supreme Court

All the litigants – NetChoice, Texas and Florida – framed the issue around the effect of the laws on the content moderation policies of the platforms, specifically whether the platforms were engaged in protected speech. The 11th U.S. Circuit Court of Appeals upheld a lower court preliminary injunction against the Florida law, holding the content moderation policies of the platforms were speech and the law was unconstitutional.

The 5th U.S. Circuit Court of Appeals came to the opposite conclusion and held that the platforms were not engaged in speech, but rather the platform's algorithms controlled platform behavior unprotected by the First Amendment. The 5th Circuit determined the behavior was censorship and reversed a lower court injunction against the Texas law.

The Supreme Court, however, reframed the inquiry. The court noted that the lower courts failed to consider the full range of activities the laws covered. Thus, while a First Amendment inquiry was in order, the decisions of the lower courts and the arguments by the parties were incomplete. The court added that neither the parties nor the lower courts engaged in a thorough analysis of whether and how the states' laws affected other elements of the platforms' products, such as Facebook's direct messaging applications, or even whether the laws have any impact on email providers or online marketplaces.

The Supreme Court directed the lower courts to engage in a much more exacting analysis of the laws and their implications and provided some guidelines.

First Amendment principles

The court held that content moderation policies reflect the constitutionally protected editorial choices of the platforms, at least regarding what the court describes as “heartland applications” of the laws – such as Facebook's News Feed and YouTube's homepage.

The Supreme Court required the lower courts to consider two core constitutional principles of the First Amendment. One is that the amendment protects speakers from being compelled to communicate messages they would prefer to exclude. Editorial discretion by entities, including social media companies, that compile and curate the speech of others is a protected First Amendment activity.

The other principle holds that the amendment precludes the government from controlling private speech, even for the purpose of balancing the marketplace of ideas. Neither state nor federal government may manipulate that marketplace for the purposes of presenting a more balanced array of viewpoints.

The court also affirmed that these principles apply to digital media in the same way they apply to traditional or legacy media.

In the 96-page opinion, Justice Elena Kagan wrote: “The First Amendment … does not go on leave when social media are involved.” For now, it appears the social media platforms will continue to control their content.

Lynn Greenky, Professor Emeritus of Communication and Rhetorical Studies, Syracuse University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Elaine Short, Tufts University

You might have heard that artificial intelligence is going to revolutionize everything, save the world and give everyone superhuman powers. Alternatively, you might have heard that it will take your job, make you lazy and stupid, and make the world a cyberpunk dystopia.

Consider another way to look at AI: as an assistive technology – something that helps you function.

With that view, also consider a community of experts in giving and receiving assistance: the disability community. Many disabled people use technology extensively, both dedicated assistive technologies such as wheelchairs and general-use technologies such as smart home devices.

Equally, many disabled people receive professional and casual assistance from other people. And, despite stereotypes to the contrary, many disabled people regularly give assistance to the disabled and nondisabled people around them.

Disabled people are well experienced in receiving and giving social and technical assistance, which makes them a valuable source of insight into how everyone might relate to AI systems in the future. This potential is a key driver for my work as a disabled person and researcher in AI and robotics.

Actively learning to live with help

While virtually everyone values independence, no one is fully independent. Each of us depends on others to grow our food, care for us when we are ill, give us advice and emotional support, and help us in thousands of interconnected ways. Being disabled means having support needs that are outside what is typical and therefore those needs are much more visible. Because of this, the disability community has reckoned more explicitly with what it means to need help to live than most nondisabled people.

This disability community perspective can be invaluable in approaching new technologies that can assist both disabled and nondisabled people. You can't substitute pretending to be disabled for the experience of actually being disabled, but accessibility can benefit everyone.

This is sometimes called the curb-cut effect after the ways that putting a ramp in a curb to help a wheelchair user access the sidewalk also benefits people with strollers, rolling suitcases and bicycles.

Partnering in assistance

You have probably had the experience of someone trying to help you without listening to what you actually need. For example, a parent or friend might “help” you clean and instead end up hiding everything you need.

Disability advocates have long battled this type of well-meaning but intrusive assistance – for example, by putting spikes on wheelchair handles to keep people from pushing a person in a wheelchair without being asked to or advocating for services that keep the disabled person in control.

The disabled community instead offers a model of assistance as a collaborative effort. Applying this to AI can help to ensure that new AI tools support human autonomy rather than taking over.

A key goal of my lab's work is to develop AI-powered assistive robotics that treat the user as an equal partner. We have shown that this model is not just valuable, but inevitable. For example, most people find it difficult to use a joystick to move a robot arm: The joystick can only move from front to back and side to side, but the arm can move in almost as many ways as a human arm.

To help, AI can predict what someone is planning to do with the robot and then move the robot accordingly. Previous research assumed that people would ignore this help, but we found that people quickly figured out that the system is doing something, actively worked to understand what it was doing and tried to work with the system to get it to do what they wanted.

Most AI systems don't make this easy, but my lab's new approaches to AI empower people to influence robot behavior. We have shown that this results in better interactions in tasks that are creative, like painting. We also have begun to investigate how people can use this control to solve problems outside the ones the robots were designed for. For example, people can use a robot that is trained to carry a cup of water to instead pour the water out to water their plants.

Training AI on human variability

The disability-centered perspective also raises concerns about the huge datasets that power AI. The very nature of data-driven AI is to look for common patterns. In general, the better-represented something is in the data, the better the model works.

If disability means having a body or mind outside what is typical, then disability means not being well-represented in the data. Whether it's AI systems designed to detect cheating on exams instead detecting students' disabilities or robots that fail to account for wheelchair users, disabled people's interactions with AI reveal how those systems are brittle.

One of my goals as an AI researcher is to make AI more responsive and adaptable to real human variation, especially in AI systems that learn directly from interacting with people. We have developed frameworks for testing how robust those AI systems are to real human teaching and explored how robots can learn better from human teachers even when those teachers change over time.

Thinking of AI as an assistive technology, and learning from the disability community, can help to ensure that the AI systems of the future serve people's needs – with people in the driver's seat.

Elaine Short, Assistant Professor of Computer Science, Tufts University

This article is republished from The Conversation under a Creative Commons license. Read the original article.