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Researchers have proposed SICMD, a method that combines two non-invasive brain signals, functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG), to identify speech a person is hearing from brain activity with a single model that works across different people. fMRI pinpoints where activity occurs and MEG captures when, together giving fuller spatiotemporal information. In cross-subject perceived-speech decoding, the method improved Top-1, Top-10 and Rankacc by more than 10.6%, 10.1% and 1.7% over baseline methods, and cut training costs by 88.8% and 60.5% compared with multi-subject and intra-subject decoding respectively. The work is a preprint and has not been peer reviewed.

Researchers have shown that language models do more to help decode imagined speech than heard speech from non-invasive brain recordings. Imagined speech is harder to read than speech a person hears: the brain's response is weaker and noisier, and less data is available. Using a magnetoencephalography (MEG) dataset in which participants both listened to and imagined the same material, the team brought a language model into both training and decoding. Imagined speech gained more from the model than heard speech, and its best results required leaning more heavily on the model. The authors conclude that the weaker the neural evidence, the more useful a language model's contextual prior becomes. The study is a preprint and has not been peer reviewed.

A brain-computer interface that times a nerve stimulus to the moment the brain prepares a wrist movement raised the excitability of the motor pathway from cortex to muscle in 17 able-bodied volunteers, whether they actually moved the wrist or only imagined it. Gains persisted 30 minutes after the session, and the two approaches did not differ significantly. The authors say this gives clinicians flexibility for patients who cannot move, though the study did not include stroke patients and individual responses varied widely.

The study tested seven detection methods to assess whether motor imagery BCIs can identify movement classes absent from training rather than force them into known categories. For many users, model uncertainty was higher for familiar classes than for unfamiliar ones, undermining methods that work well in other machine learning domains. Detection performance was correlated with performance on the trained task. Deep Ensemble and Monte Carlo-Dropout models achieved an on-task AUC of 0.9 and out-of-distribution detection ability up to 0.7. The authors conclude that rejecting unfamiliar cognitive states is feasible for models and participants with high task performance.

A collaboration involving the University of Cambridge analyzed surface electrocorticography recordings from the human ventral anterior temporal lobe. It found significant decoding of graded, multidimensional semantic information when signals across 4-200 Hz were considered together, but not from individual frequency bands. Using representational similarity learning on time-frequency power and phase data, the researchers compared three hypotheses about how semantic information maps onto signal frequencies. The authors interpret the findings as supporting transfrequency coding, in which some information emerges only when multiple frequencies are considered jointly. The study has not been peer reviewed.

Precision Neuroscience closed an oversubscribed $250 million Series D co-led by Pershing Square Inc., the Ackman Oxman Institute and an undisclosed life sciences fund, bringing its total raised since 2021 to $430 million. The company says it has completed more than 100 patient procedures at 18 medical institutions and holds FDA clearance for its Layer 7 Cortical Interface, a thin-film electrode array placed on the brain surface. It plans to use the money to expand clinical work, move Layer 7 through the next stages of FDA review and build out commercialization.

Researchers propose DenoFlow, an EEG denoising method designed to remove interference from muscle and eye electrical activity while preserving information that a brain-computer interface can decode. The authors report better signal fidelity and decoding accuracy than seven baseline denoising models in tests on two public steady-state visual evoked potential (SSVEP) datasets with five decoders. They created paired contaminated and original signals by injecting recorded muscle and eye artifacts at controlled signal-to-noise targets. The findings come from these controlled dataset tests, and the study has not been peer reviewed.

A single-author study presents a hybrid algorithm for telling apart spikes from the many neurons an implanted electrode records at once, a step a brain-computer interface (BCI) must take before it can decode intent. A deep neural network assigns each spike to a neuron and estimates its waveform, fuzzy-logic methods detect spikes and score confidence when signal quality is uncertain, and the network architecture was chosen by multi-objective selection. On four datasets from rat cortex, hippocampal CA1 and monkey motor cortex, it reached 96.4% accuracy, 94.8% precision and a mean latency of 45.2 ms with full labels, and 88.9% accuracy with only 10% of data labeled, which the author says is comparable to existing unsupervised methods while also providing confidence and waveform estimates. The author notes that prospective validation is needed before clinical use.

A team including the University of Science and Technology Beijing reviewed the open-source software ecosystem for non-invasive, EEG-based brain-computer interfaces, which read brain signals through scalp electrodes without surgery. The review covers four dimensions, technical foundations, tool taxonomy, scenario adaptation and future evolution, shows how open-source tools span the full workflow from signal acquisition to real-time interaction, classifies them by application such as healthcare, education and entertainment, and identifies the ecosystem's current technical challenges, offering developers a method for choosing tools.

Researchers re-examined a multi-class imagined speech EEG dataset, words spoken silently in the mind, under a strictly subject-independent protocol in which a model is never trained on data from the person it is tested on. Spectral bandpower features reached a mean trial-wise accuracy of 49.03% ± 4.18% in coarse-level classification, clearly ahead of the 37.97% ± 3.79% from time-domain statistical features, and a few frequency bands carried most of the discriminative information. The study is a preprint and has not been peer reviewed.