How do distributed neural circuits drive purposeful movements from the complex musculoskeletal system? This characterization is critical towards not just furthering our understanding of the generation of movement, but, importantly, guiding us towards therapeutic targets for diseases affecting motor control. The neural processes leading to movements have been relatively well posited and understood due to the quantitative nature of the behavioral outputs involved. Classic approaches have largely focused on optimization principles, including limb control, to achieve human-like behavioral trajectories. These largely theoretical models of sensorimotor control can recapitulate observed movement trajectories by hypothesizing the presence of a controller guiding the complex movements. However, these models cannot predict how neuronal populations in each brain region affects the resulting movement and vice-versa. On the other hand, breakneck advances in hardware techniques have led to vast improvements in our ability to record large-scale multi-regional neural data. These recordings have enabled dimensionality reduction and modeling techniques to elucidate the structure in high-dimensional neural activity during different conditions, and relate the neural activity directly to kinematic outcomes. However, these data-driven models typically do not consider the biophysical underpinnings of the musculoskeletal system, and thus fail to elucidate the computational role of neural activity in driving the musculoskeletal system such that the body reaches a desired state.

'CyberGuy' Kurt Knutsson shares his tips on reducing daily internet usage after an eye-opening poll reveals Americans spend nearly half their day online on'Fox & Friends.' Audrey Crews hasn't written her name in over 20 years, that is, until now. Thanks to a brain chip from Elon Musk's Neuralink, Crews used only her thoughts to scribble "Audrey" on a laptop screen. She shared the photo on X, stunning millions across the internet and earning a shoutout from Musk himself. At age 16, Crews lost all motor function. Now, at 39, she's part of Neuralink's PRIME Study, which tests brain-computer interface (BCI) technology in humans.

A woman who has been fully paralyzed for the last 20 years has regained the ability to use a computer, marking a world-first for Elon Musk's company, Neuralink. Thanks to Neuralink's revolutionary implant, Audrey Crews revealed on X how she was able to write her name on a computer screen. 'I tried writing my name for the first time in 20 years. Lol,' Crews posted on X while showing the world her first attempt at a signature since 2005. Using the brain-computer interface (BCI), the implant recipient chose a purple-colored cursor pen to write the name'Audrey' on the screen in cursive script.

Current state-of-the-art dynamical models, such as Mamba, assume the same level of noisiness for all elements of a given sequence, which limits their performance on noisy temporal data. In this paper, we introduce the $\alpha$-Alternator, a novel generative model for time-dependent data that dynamically adapts to the complexity introduced by varying noise levels in sequences. The $\alpha$-Alternator leverages the Vendi Score (VS), a flexible similarity-based diversity metric, to adjust, at each time step $t$, the influence of the sequence element at time $t$ and the latent representation of the dynamics up to that time step on the predicted future dynamics. This influence is captured by a parameter that is learned and shared across all sequences in a given dataset. The sign of this parameter determines the direction of influence. A negative value indicates a noisy dataset, where a sequence element that increases the VS is considered noisy, and the model relies more on the latent history when processing that element. Conversely, when the parameter is positive, a sequence element that increases the VS is considered informative, and the $\alpha$-Alternator relies more on this new input than on the latent history when updating its predicted latent dynamics. The $\alpha$-Alternator is trained using a combination of observation masking and Alternator loss minimization. Masking simulates varying noise levels in sequences, enabling the model to be more robust to these fluctuations and improving its performance in trajectory prediction, imputation, and forecasting. Our experimental results demonstrate that the $\alpha$-Alternator outperforms both Alternators and state-of-the-art state-space models across neural decoding and time-series forecasting benchmarks.

Multiple brain-computer interface (BCI) projects are currently underway, but BrainGate is one of the first aimed at motor restoration in users affected by neurodegenerative disorders and spinal cord injuries. Researchers have spent years working through the device's clinical trial phases, but their most recent breakthrough isn't focused on physical accomplishments. Instead, the latest achievements could pave the way for people with disabilities to more easily utilize complex computer software, communicate with loved ones, work remotely, and even make music. According to a study published by BrainGate engineers on January 20 in the journal Nature Medicine, a volunteer with quadriplegia can now maintain unprecedented control over a virtual object using their surgically implanted BrainGate BCI device. To demonstrate the ability, the patient guided a virtual rotocopter through hoops in a digital obstacle course by simply thinking about moving the fingers on one of their hands.

Elon Musk captured the imaginations of the world this week when he revealed one of the first volunteers to have his brain chip implanted in their skulls. But the historic moment is only possible thanks to decades of pioneering scientists and brave subjects that came before it - who had brain-computer interface chips into people's brains, with much more primitive tech. Musk has said he hopes that in the very near future his Neuralink device will enable people to control a computer cursor or keyboard with their brain to communicate, like'replacing a piece of the skull with a smartwatch.' Musk has said he hopes that in the very near future his Neuralink device will enable people to control a computer cursor or keyboard with their brain to communicate, like'replacing a piece of the skull with a smartwatch.' Ultimately, brain-computer interface devices offer the promise of giving disabled people the ability to see, touch, speak, and perform tasks again - and some proponents like Musk see an ultimate goal of all of humanity merging with tech in future. His device builds on the foundation built by tech that was pioneered in 2006 and allowed a paralyzed man to move a computer mouse with his brain a whole decade before Neuralink was founded.

Public Motor Imagery-based brain-computer interface (BCI) datasets are being used to develop increasingly good classifiers. However, they usually follow discrete paradigms where participants perform Motor Imagery at regularly timed intervals. It is often unclear what changes may happen in the EEG patterns when users attempt to perform a control task with such a BCI. This may lead to generalisation errors. We demonstrate a new paradigm containing a standard calibration session and a novel BCI control session based on EMG. This allows us to observe similarities in sensorimotor rhythms, and observe the additional preparation effects introduced by the control paradigm. In the Movement Related Cortical Potentials we found large differences between the calibration and control sessions. We demonstrate a CSP-based Machine Learning model trained on the calibration data that can make surprisingly good predictions on the BCI-controlled driving data.

A man with Lou Gehrig's disease, also known as ALS, is the 10th person to receive a brain chip that lets him take control of his life using just his mind. Mark, 67, was diagnosed in 2020 and has slowly lost his physical abilities like accessing his phone or feeding himself, but that soon to change after receiving Synchron brain-computer interface (BCI) last August. ALS is a disease that causes nerve cells to deteriorate and results in muscle weakness and reduced dexterity until the person is eventually paralyzed - the entire process can take two to five years, and there is no cure. Mark is now able to send health notifications or pain reports to his provider using just by the BIC reading his brainwaves and translating them into actions carried out on a computer. He will soon be able to use his thoughts for more exciting tasks like turning on Netflix and texting family and friends.

Writing something down rather than typing it on a computer could help you retain the information better, after researchers found putting pen to paper boosts connectivity between different areas of the brain. Using a keyboard – whether on a computer, laptop or smartphone – is a relatively quick and easy way to write. Already standard in offices, some students are increasingly using computers and laptops at school. But these modes of writing are very different, says Audrey van der Meer at the Norwegian University of Science and Technology. "When you type on the keyboard, you only make very simple finger movements towards the keys and they're exactly the same movements for every letter you want to write."

With the London Marathon coming up this weekend, many may be wondering if we will see a runner achieve a time under two hours. The world record for the fastest 26.2 mile (42.2 km) run is 2 hours, 1 minute and 9 seconds, as set by Eliud Kipchoge during the 2022 Berlin Marathon. He actually beat this time, and achieved the elusive sub-two hour milestone, three year's prior in a park in Vienna, Austria, but this was not recognised as a record. The London race would meet the record requirements if someone beat Kipchoge's time, and with technological advancements, we are closer than we have ever been. Here, MailOnline takes a look at some of the unusual technologies and inventions that may one day help an athlete finally reach the finish line in under two hours.