Papers

Precision Neuroscience announced a human central nervous system atlas combining high-resolution surface electrical recordings from 70 patients at 12 academic medical centers. It spans multiple cortical regions as well as brainstem and spinal cord structures. The company says the atlas includes more than 180 hours of synchronized neural and behavioral data and demonstrates motor decoding, spoken-word classification, and classification of eight facial expressions. The manuscript describes a 70-patient cohort; the broader total of patients implanted with the company's system had surpassed 100 by the announcement. These are distinct counts. The findings were disclosed by the company in connection with a preprint that has not been peer reviewed.

The study proposes inferring cognitive states using brain-intrinsic prior constraints rather than simply mapping neural signals to predefined external labels. Across five neural recording modalities and three cognitive domains—motor, perception and internal mentation—the method reorganized neural representations. The resulting high-level semantic representations showed consistent geometric relationships across tasks and enabled recovery of stable cognitive states from variable observations. The authors interpret these findings as suggesting that relatively stable cognition may be maintained through inference without fixed neural activity patterns; this interpretation is distinct from the experimental observations.

A University of Ulsan study analyzed two independent public datasets to examine when brain rhythms begin to weaken across regions during cue-paced motor imagery and when information distinguishing imagined movements emerges. Regional changes followed an ordered sequence. Class-discriminative information rose most steeply 0.60-0.75 seconds after the cue and saturated within a further 0.3 seconds. In every participant across both datasets, this transition was closer to sensorimotor desynchronization onset than to cue presentation. However, windows centered on each region's own onset classified equivalently to reassigned windows. The authors interpret the sequence as a timing landmark for analysis rather than a directly decodable feature.

A contextual prior can raise overall accuracy in MEG speech decoding while making some correctable errors more confident, a preprint reports. Tracking candidate scores across two magnetoencephalography datasets, the authors describe a reversal: when the correct candidate starts near the top of the local ranking, a larger fused margin makes a repair more likely, but when it starts lower, a larger margin makes the error more likely to persist. On MEG-MASC, pooled correctness AUROC is 0.87, while the AUROC separating repairs from residual errors falls from 0.70 to 0.39; errors starting beyond rank 20 account for 46.6% of post-fusion errors. The authors argue confidence should retain local and contextual evidence rather than fused scores alone; reading the two scores separately lifts decoder coverage to 74.5% of windows from 56.7%. The study is a preprint and has not been peer reviewed.

Rats implanted with microwire arrays showed an exponential-like fall in intracortical microstimulation (ICMS) perception thresholds from weeks 5 to 12, then held near a fitted asymptote of about 1.1 nC/ph per electrode across 10 stimulating electrodes through week 30 in animals that retained detectable percepts. Correct responses to the conditioned stimulus stabilized at roughly 90%. Neither the arrays' electrophysiological nor their electrochemical properties explained the threshold trend across animals. Gene expression in implanted tissue showed elevated Gfap and Cd68 alongside reduced NeuN and Bdnf, consistent with earlier neuroinflammation findings. Two animals lost sensation over the 30-week period.

EEG-based brain-computer interfaces lose accuracy when the user or session changes, because the signal distribution shifts, and users usually have to sit through labeled calibration. SPDAlign tackles this without labels. The researchers show theoretically that shifts caused by the forward process, linked to factors such as head geometry, can be recovered solely through linear transformations on the Symmetric Positive Definite manifold. The framework aligns domain-specific means, then corrects global rotations with an optimal transport technique called Wasserstein Procrustes. The authors report competitive performance on public EEG datasets and say the method can identify relevant frequency ranges and spatial patterns. The study is a preprint and has not been peer reviewed.

EEG signals drift, so a brain-computer interface model calibrated in the lab often loses accuracy in everyday use. This preprint puts Common Spatial Patterns (CSP) and Riemannian covariance-based pipelines through the same time-ordered evaluation: predict first, then update after the true label arrives. Across 4 public datasets, label-revealed updates lifted accuracy in 13 of 14 model/dataset pairs on the 2 largest streams, up to about 18% over a frozen model. A Shapley-based valuation gave the most recent time block the highest mean value in all 3 datasets analyzed, while older blocks retained positive value.

Researchers recorded EEG during overt and imagined vocalisation to distinguish five emotion categories: anger, happiness, neutral, sadness, and pleasure. The overt experiment involved 14 participants and the imagined experiment involved 21, yielding single-trial decoding accuracies of 78.1% and 36.4%, respectively, against 20% chance. Facial surface EMG recorded in five participants during imagined vocalisation indicated that involuntary muscle activity contributed to EEG spectral and spatial patterns used for classification. One lateral-channel pattern was stronger during imagined happy vocalisations. The results mean that decoding accuracy cannot be attributed entirely to brain-derived emotion information.

An ISAE-SUPAERO study tested a brain-computer interface combining semi-dry EEG acquisition with visual stimulation. Thirty participants completed an 11-class keypad typing task on a standard screen. With 33 s of calibration data, the system achieved 99.8% accuracy and a mean decoding time of 4.3 s. No spurious commands occurred during a 2-min video-viewing idle condition between two online typing sessions. A virtual reality proof of concept reached 100% online accuracy with a mean decoding time of 5.6 s. Participants reported high visual comfort and little peripheral distraction in both conditions, but greater fatigue in VR. The short idle test does not establish long-term performance.

EEG signals vary between people and drift over time, so a brain-computer interface usually has to be recalibrated for each new user. Researchers at Hangzhou Dianzi University built TSMD-Net, which learns from several existing subjects without any data from the new user, then pools what it learned about each one into a single classifier. It reached mean accuracies of 70.21%, 77.82% and 79.53% on three public EEG datasets.

A stretchable EEG platform called MindStretcH maintained stable scalp contact through hair over prolonged use, with no measurable drop in recording quality across repeated online brain-computer interface sessions, researchers at The University of Texas at Austin reported. The system combines a kirigami-patterned elastomer mesh, liquid-metal interconnects and detachable hydrogel electrodes. The abstract does not disclose participant numbers or session duration.

Researchers found that a decoder trained on peripheral physiological signals (heart rate, heart rate variability, respiration, electrodermal activity and pupil diameter) read arousal more accurately than EEG, reaching a within-subject area under the curve (AUC) of 93.0%, versus 85.2% for an EEG-only decoder and 79.8% for the original filter-bank common spatial pattern decoder. The offline analysis used a public simulated-flight dataset. Only the peripheral decoder reproduced the inverted-U relationship between arousal and performance, and EEG neurofeedback did not change overall arousal trajectories, suggesting its effect may come from regulation at task-critical moments. The study is a preprint and has not been peer reviewed.

When a user looks at targets flickering at different frequencies, the visual cortex produces EEG responses at matching frequencies, known as steady-state visual evoked potentials (SSVEP), which a brain-computer interface can use to infer the intended target. Such systems usually need calibration data, and more data means more user fatigue. Researchers propose SCR-FTSDNN, a deep convolutional network that moves EEG into the frequency domain, then extracts temporal and spatial features with added spatial and channel reconstruction modules. Tested on the public Benchmark and BETA datasets, the authors report higher recognition accuracy and stability than several existing methods, especially with limited calibration data. The abstract gives no specific figures, and the results come from offline tests.

Would drivers accept a car that reads their brain signals and steps in when they are overloaded? In an online experiment, 1201 participants each viewed 1 of 12 animated driving scenarios. In each scenario, a wearable, non-invasive brain-computer interface detected high workload and triggered assistance. Acceptance varied systematically across scenarios. It was higher when the system supported driving-related functions and when the driver shown had a disability. The 2 automation activation modes tested made no difference.

A study combines EEG data with multilingual text to classify emotions, since EEG alone is prone to noise while language models read sentiment in words but miss implicit changes in the brain. The author reports 89.7% accuracy for the fused model, versus 75% for EEG alone and 79% for text alone, with an F1-score of 0.88 and an average inference latency of 180 ms, enough for real-time recognition. The abstract does not state the number of participants or the datasets used.

Researchers have proposed CortexBridge, a lightweight adapter that lets EEG foundation models, usually pretrained on a single fixed electrode layout, handle any montage. It combines EEG features with electrode and brain-atlas coordinates to map signals into a shared cortical latent space before passing them to the pretrained model, which is left unchanged. On 5 BCI datasets from the public MOABB benchmark, tested with 3 frozen foundation models, EEGPT, LaBraM and CBraMod, it improved performance in 13 of 15 evaluations, raising balanced accuracy by an average of 0.80%, 0.70% and 3.26% respectively, with a maximum gain of 13.02% on 12-class steady-state visually evoked potential classification. The study is a preprint and has not been peer reviewed.

Researchers combined high-resolution surface electrophysiology recordings from the first 70 patients implanted with an anatomically conformal, thin-film neural interface to build a functional atlas of the human central nervous system. The interface was positioned at more than 100 locations, with >100,000 electrode deployments spanning motor, sensory, language and other functional regions, as well as the brainstem and spinal cord. Aggregated recordings showed spatially ordered digit representations, movement-related frequency-band changes and task-modulated neural population activity. The study also demonstrated real-time neuroprosthetic control and decoding of cortical activity associated with speech, facial movements and emotional expressions. This approach combines recordings across patients rather than simultaneously reading the entire central nervous system in one person. This study is a preprint and has not been peer reviewed.

Color vision tests usually rely on what the subject reports, while a brain-computer interface can read the brain's response to color differences directly: the more two flickering light sources differ in perceived color, the stronger the steady-state visual evoked potential in the EEG. Existing systems sample color space evenly and repeat measurements at each point, which is accurate but slow. MetaIDNet, a closed-loop system, instead uses Gaussian process regression and a neural network to decide where to measure next. Offline, it made assessment 70% faster at the same accuracy; online, it improved accuracy by 40% at the same speed. The offline and online experiments involved only 10 and 8 participants.

Researchers have proposed NEUROTOKEN, a single network that uses conditional flow matching to score candidate voices and identify, from EEG, which speaker a listener is attending to in a noisy room, the so-called cocktail-party problem. On the public KU Leuven, DTU and NJU datasets with 5-second windows, it raised per-segment accuracy by 9% to 16% over the strongest non-generative baseline and narrowed across-subject variance by about 3x. The authors also report that the 95% to 97% accuracies often cited for decoding attention direction fall by 17% to 45% once training and test trials are strictly separated. The work is a preprint and has not been peer reviewed.

Researchers have released BrainNet Studio, a single toolkit that takes brain network data from construction and feature extraction through predictive modeling and visualization. Brain networks map structural and functional links between brain regions, but most existing tools handle only static networks and miss how those links change over time. The toolkit bundles 27 algorithms spanning deep learning, graph neural networks and spatiotemporal sequence models, and uses a large language model to generate connectivity summaries that researchers can check. The developers say it can support cognitive neuroscience, exploratory brain disorder studies and brain-computer interface research. The work is a preprint and has not been peer reviewed.

An offline EEG study of 15 participants found that on-screen flickering targets stepping up or down in frequency can be recognized as brain-control commands, but they did not classify more accurately than conventional fixed-frequency flicker. Mean normalized accuracies were 89.7% for fixed frequency, 85.6% for ascending and 89.5% for descending patterns, a statistically insignificant difference. Low-frequency stimulation at 6–10 Hz reached 90.8%, versus 80.6% at 41–45 Hz. Sequences of three consecutive commands averaged 91.5 ± 9.6%, with accuracy falling as sequences grew longer. The researchers see the main value in more flexible design of consecutive commands and say larger samples and online closed-loop testing are needed.

Researchers have developed an adaptive version of Artifact Subspace Reconstruction (ASR) that removed 53.1% of blink artifacts from EEG recordings, compared with 24.0% for the original ASR and 26.7% for a limited-memory variant. EEG signals drift over time and are often contaminated by blinks and other artifacts, a problem BCIs must solve for long-term use in real-world settings. Standard ASR suppresses artifacts automatically based on variance but relies on a static covariance model derived from calibration data, which becomes less robust as signals drift. The new method tracks signal structure with an exponential moving average, combining short-term covariance estimates with slow recursive updates so the model adapts on a fast and a slow timescale at once. It was tested on 24-channel EEG from 10 subjects across different cognitive states. The trade-off was larger reconstruction and spectral deviation. The study is a preprint and has not been peer reviewed.

Researchers testing 3 EEG foundation models, general-purpose models pretrained on large amounts of EEG data, on 235 subjects excluded from training found that a lightweight adapter trained for each individual raised mean balanced accuracy by 1.5 to 5.4 percentage points over the shared population model. Every new brain-computer interface user needs calibration, and a personally tuned model may be more accurate either because it captures the individual's traits or simply because the underlying general model is stronger. Personal adapters also beat adapters borrowed from other people by 2.3 to 7.3 points. When the population model's training was scaled up fourfold, the personalization gain stayed positive but narrowed. Low-cost calibration using only a few labeled samples gave inconsistent results. The study is a preprint and has not been peer reviewed.

Researchers have proposed BrainVLA, a framework that borrows a vision-language-action model pretrained on large amounts of robot data to decode what a paralyzed user intends to do. A key obstacle for BCIs that control external devices is the scarcity of paired neural and movement data; the usual approach of learning a task-specific map from neural activity to kinematic parameters is held back by that shortage. The team first built a paired dataset of neural activity, action signals, language instructions and rendered visual observations, then adapted the OpenVLA-OFT policy to the target action space through LoRA fine-tuning. Neural activity is aligned with language representations to extract an intention signal, which the model combines with visual input to generate executable actions. On 2 neural motor datasets with different action dimensions, the framework outperformed the evaluated baselines in cross-session decoding R² and task success rate, and was data-efficient to train. The study is a preprint and has not been peer reviewed.

Researchers have built AutoBCI, a framework in which AI agents design EEG decoding models, and report that it slightly outperformed the strongest existing model across 14 datasets. Building a decoder that works well across many EEG tasks usually takes repeated trial and error. AutoBCI splits the job between 2 agents: one generates and refines architectures, while the other forecasts final performance from early learning curves to pick out candidates worth training further. Across 14 EEG datasets covering motor imagery, emotion recognition and sleep staging, the architecture found with Claude Opus 5.5 reached an average test balanced accuracy of 64.16%, slightly above the 63.87% of REVE, the strongest baseline. Looking at only 10 training epochs, the forecasting agent cut prediction error from 2.20 to 1.36 percentage points. The study is a preprint and has not been peer reviewed.

When a motor imagery BCI is used on a different day, EEG signals drift, yet a standard classifier keeps issuing commands regardless of confidence. Researchers at the University of Notre Dame, reporting in the Journal of Neural Engineering, added a conformal prediction gate that withholds commands when the model is unsure. Using DeepConvNet as the base classifier, the gate cut the share of wrong commands from 37.5% to 9–16% on the OpenBMI dataset (54 subjects) and from 35.5% to 6–8% on BCI Competition IV Dataset 2a (9 subjects), at the cost of issuing fewer commands. All results come from offline public datasets.

Researchers at Bishop’s University compared visual decoding using EEG, MEG, intracranial EEG, and fMRI. Individual differences were reproducible within an instrument, but external paired cohorts did not show a common participant ranking across instruments. Classifiers were tested on recording runs and stimulus identities excluded from training. Attention, behavior, and peripheral signals affected recording quality but did not explain most stable differences. Intracranial recordings also linked sharper local category selectivity to easier decoding. The authors interpret these differences as reflecting the relationship between the individual, the distinction being represented, and the instrument, rather than a fixed personal trait alone.