The company claims that its new device, the Neurable One, can keep you in the zone, calculate the best time to take a break, and tell you when your "brain battery" needs a recharge. Neurable unveiled its very own pair of brain-scanning headphones on Thursday, the Neurable One . The company claims that wearing them helps you concentrate on whatever you're doing--working, studying, or playing a video game. When I spoke to Neurable earlier this year, the company said it planned to license its brain-scanning technology to power a flood of gadgets that were ready to enter the market. But it decided to launch its own hardware first.

A new AI tool can guess what you're looking at just by analyzing your brain scans--and recreate that image with remarkable precision. It can go the other way too, and predict a person's brain activity based on what they're looking at. In the image above, for example, the left-hand image of each pair is what the user actually saw--and its right-hand counterpart is what the model recreated based on the brain scan. Michal Irani, who developed the tool with her colleagues at the Weizmann Institute of Science in Rehovot, Israel, hopes her "mindreading" tool will ultimately reveal more about how the brain works, and could perhaps be used to help locked-in people communicate, or allow scientists to recreate the content of dreams. Judy Illes, a neuroethicist and professor of neurology at the University of British Columbia in Canada, who was not involved in the research, describes the work as "magnificent." "The idea [of using this approach] to help people with neurologic conditions therapeutically is tremendously exciting," she says. But other scientists warn that a similar approach could be used to reveal the inner thoughts and mental imagery of people, potentially without their consent. "The results seem very impressive," says Tommy Sprague, a neuroscientist at the University of California Santa Barbara. "But if there's a way to surreptitiously extract information about what you're thinking about, then 150 years of sci-fi can come true anytime, and that's worrisome in a lot of ways."

Psychedelics are often associated with disconnecting from reality, but a recent neuroimaging study found that psilocybin can actually make our brain activity more attuned to the world around us. That's what scientists have generally thought, with several studies showing that when we take these substances, the signals pinging between our neurons become more diverse and unpredictable. This increased entropy in our gray matter may underlie the strange, random, and often memorable experiences of psychedelic trips. But in one of the largest neuroimaging studies on the effects of psilocybin to date, researchers uncovered a hidden order in the apparent chaos. The study, published in Nature, indicates that while psilocybin blurs some of the boundaries within the brain, our neurological reactions to what we're experiencing in that moment become even more distinct.

Silicon Valley companies are already working on neurotechnology products that track your brain activity. The next privacy frontier might be the things you only think. Rafael Yuste is in his early sixties and bears a more than passing resemblance to Pablo Picasso--if Picasso had worn glasses and had a trim white goatee. Speaking succinctly and methodically, his accent rich with Spanish inflections, he told me about an experiment he had carried out in his lab at Columbia on the brains of mice, and specifically on that part of the cortex that responds to vision. His mentor had been the Swedish neuroscientist Torsten Wiesel, who won a Nobel Prize for his research into how our visual systems process information. "He discovered by chance that the strongest stimulus is a pattern of high-contrast dark and light bars." He held up one hand and waved his fingers back and forth. "If you imagine my fingers were bars of light surrounded by complete blackness--if I move my fingers in front of your eyes, that fires up your whole visual cortex."

The colorful and customizable wearables can monitor heart rate, brain activity, and more. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Using a conductive, face-paint-like ink, researchers can now paint electrodes to monitor a wearer's heart, muscle or brain activity in style. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Recent studies have demonstrated that fine-tuning language models with brain data can improve their semantic understanding, although these findings have so far been limited to English. Interestingly, similar to the shared multilingual embedding space of pretrained multilingual language models, human studies provide strong evidence for a shared semantic system in bilingual individuals. Here, we investigate whether fine-tuning language models with bilingual brain data changes model representations in a way that improves them across multiple languages. To test this, we fine-tune monolingual and multilingual language models using brain activity recorded while bilingual participants read stories in English and Chinese. We then evaluate how well these representations generalize to the bilingual participants' first language, their second language, and several other languages that the participants are not fluent in. We assess the fine-tuned language models on brain encoding performance and downstream NLP tasks. Our results show that bilingual brain-informed fine-tuned language models outperform their vanilla (pretrained) counterparts in both brain encoding performance and most downstream NLP tasks across multiple languages. These findings suggest that brain-informed fine-tuning improves multilingual understanding in language models, offering a bridge between cognitive neuroscience and NLP research. We make our code publicly available.

Decoding visual signals holds an appealing potential to unravel the complexities of cognition and perception. While recent reconstruction tasks leverage powerful generative models to produce high-fidelity images from neural recordings, they often pay limited attention to the underlying neural representations and rely heavily on pretrained priors. As a result, they provide little insight into how individual voxels encode and differentiate semantic content or how these representations vary across subjects. To mitigate this gap, we present an insightful Multi-subject Invariant Neural Decoding (iMIND) model, which employs a novel dual-decoding framework-both biometric and semantic decoding-to offer neural interpretability in a data-driven manner and deepen our understanding of brain-based visual functionalities. Our iMIND model operates through three core steps: establishing a shared neural representation space across subjects using a ViT-based masked autoencoder, disentangling neural features into complementary subject-specific and object-specific components, and performing dual decoding to support both biometric and semantic classification tasks. Experimental results demonstrate that iMIND achieves state-of-the-art decoding performance with minimal scalability limitations. Furthermore, iMIND empirically generates voxel-object activation fingerprints that reveal object-specific neural patterns and enable investigation of subject-specific variations in attention to identical stimuli. These findings provide a foundation for more interpretable and generalizable subject-invariant neural decoding, advancing our understanding of the voxel semantic selectivity as well as the neural vision processing dynamics.

However, their widespread adoption is hindered by critical limitations, such as low information transfer rates and extensive user-specific calibration. To overcome these challenges, recent research has explored the integration of Large Language Models (LLMs), extending the focus from simple command decoding to understanding complex cognitive states. Despite these advancements, deploying agentic AI faces technical hurdles and ethical concerns. Due to the lack of comprehensive discussion on this emerging direction, this position paper argues that the field is poised for a paradigm extension from BCI to Brain-Agent Collaboration (BAC). We emphasize reframing agents as active and collaborative partners for intelligent assistance rather than passive brain signal data processors, demanding a focus on ethical data handling, model reliability, and a robust human-agent collaboration framework to ensure these systems are safe, trustworthy, and effective.

Decoding of visual stimuli from noninvasive neuroimaging techniques such as functional magnetic resonance (fMRI) has advanced rapidly in the last years; yet, most high-performing brain decoding models rely on complicated, non-interpretable latent spaces. In this study we present an interpretable brain decoding framework that inserts a semantic bottleneck into BrainDiffuser, a well established, simple and linear decoding pipeline. We firstly produce a 214 dimensional binary interpretable space L for images, in which each dimension answers to a specific question about the image (e.g., "Is there a person?",

Casey Harrell has had a set of electrodes embedded in his brain for almost three years. Harrell, who has amyotrophic lateral sclerosis (ALS) and is paralyzed, first used his brain-computer interface (BCI) to "speak" sentences with the help of a research team in 2023. Since then, Harrell has clocked thousands of hours of use. He can use the device largely independently, once he's been "plugged in" with the help of a carer. His team has added new features to it, and Harrell also uses it to surf the web and perform his job.