The startup Coherence Neuro is now testing a brain-computer interface that could one day use electrical stimulation to prevent tumors from growing. A San Francisco startup with ties to Elon Musk's Neuralink has started testing its brain implant to detect and treat cancer in humans. Coherence Neuro says it temporarily placed its coin-sized implant in the brains of three people undergoing surgery to have brain tumors removed at the Royal Melbourne Hospital in Australia. The implant was in place for roughly 30 minutes before being removed, providing an important safety check before the device can be implanted long-term in patients with brain cancer. Known as a brain-computer interface, the Coherence Neuro device is designed to sense the unique electrical signals of tumors and deliver mild electrical stimulation to prevent their growth.

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.

Electroencephalography (EEG) provides access to neuronal dynamics noninvasively with millisecond resolution, rendering it a viable method in neuroscience and healthcare. However, its utility is limited as current EEG technology does not generalize well across domains (i.e., sessions and subjects) without expensive supervised re-calibration. Contemporary methods cast this transfer learning (TL) problem as a multi-source/-target unsupervised domain adaptation (UDA) problem and address it with deep learning or shallow, Riemannian geometry aware alignment methods. Both directions have, so far, failed to consistently close the performance gap to state-of-the-art domain-specific methods based on tangent space mapping (TSM) on the symmetric, positive definite (SPD) manifold. Here, we propose a machine learning framework that enables, for the first time, learning domain-invariant TSM models in an end-to-end fashion. To achieve this, we propose a new building block for geometric deep learning, which we denote SPD domain-specific momentum batch normalization (SPDDSMBN). ASPDDSMBN layer can transform domain-specific SPD inputs into domain-invariant SPD outputs, and can be readily applied to multi-source/-target and online UDA scenarios. In extensive experiments with 6 diverse EEG brain-computer interface (BCI) datasets, we obtain state-of-the-art performance in inter-session and -subject TL with a simple, intrinsically interpretable network architecture, which we denote TSMNet.

Invasive brain-computer interfaces with Electrocorticography (ECoG) have shown promise for high-performance speech decoding in medical applications, but less damaging methods like intracranial stereo-electroencephalography (sEEG) remain underexplored. With rapid advances in representation learning, leveraging abundant recordings to enhance speech decoding is increasingly attractive. However, popular methods often pre-train temporal models based on brain-level tokens, overlooking that brain activities in different regions are highly desynchronized during tasks. Alternatively, they pre-train spatial-temporal models based on channel-level tokens but fail to evaluate them on challenging tasks like speech decoding, which requires intricate processing in specific language-related areas. To address this issue, we collected a well-annotated Chinese word-reading sEEG dataset targeting language-related brain networks from 12 subjects. Using this benchmark, we developed the Du-IN model, which extracts contextual embeddings based on region-level tokens through discrete codex-guided mask modeling. Our model achieves state-of-the-art performance on the 61-word classification task, surpassing all baselines. Model comparisons and ablation studies reveal that our design choices, including (\romannumeral1) temporal modeling based on region-level tokens by utilizing 1D depthwise convolution to fuse channels in the ventral sensorimotor cortex (vSMC) and superior temporal gyrus (STG) and (\romannumeral2) self-supervision through discrete codex-guided mask modeling, significantly contribute to this performance. Overall, our approach -- inspired by neuroscience findings and capitalizing on region-level representations from specific brain regions -- is suitable for invasive brain modeling and represents a promising neuro-inspired AI approach in brain-computer interfaces.

While the United States and Europe are moving cautiously forward with clinical trials, China is racing toward the commercialization of brain implants. China has made history by becoming the first nation to approve a commercially available brain chip to treat a disability. NEO, the implant developed by Neuracle Medical Technology, translates the thoughts of a person with paralysis into movements of an assistive robotic hand. After 18 months of testing that proved its safety, China's National Medical Products Administration authorized the implant for people aged 19 to 60 with paralysis caused by neck or spinal cord injuries that prevent them from moving their limbs. According Nature, the implant embedded in the skull is about the size of a coin.

Brain-computer interfaces (BCIs) have enabled prosthetic device control by decoding motor movements from neural activities.

Furthermore,analysis of model interpretation reveals the capability of MAtt in capturing informative EEGfeatures andhandling thenon-stationarity ofbraindynamics.