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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 The study is a preprint and has not been peer reviewed.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.

After a stroke, can the brain's intent to move still drive arm rehabilitation? A trial registered by Tan Tock Seng Hospital will read scalp EEG as acute stroke patients attempt movement and use those signals to trigger functional electrical stimulation of the arm. The study focuses on how these brain signals change over time after stroke and what that means for BCI-based rehabilitation. It plans to enroll 12 patients, is not yet recruiting, and expects primary completion in December 2027.

Brain-computer interfaces (BCIs) face human factors bottlenecks such as reliability and usability as they move toward real-world use, yet human factors engineering has mostly studied overt behavior and peripheral physiology rather than brain activity as a control input. This conceptual review proposes Human Factors Engineering for BCIs as an interdisciplinary direction, centered on user neurocognitive traits, brain-computer co-adaptation, multimodal feedback and context, with 3 core scientific questions and ethics, privacy and user rights as an overarching dimension.

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.

Researchers in Germany fitted participants with lightweight EEG electrodes around the ear and a portable hearing aid research platform, then tested auditory attention while they sat and walked. Speech reconstructed from the EEG followed earlier lab findings: most accurate for a single talker, then the attended talker of two, and least for the ignored one. Walking introduced a speech-synchronized artifact, possibly from hearing aid cables touching the electrodes, which affected accuracy; the researchers conclude feasibility is shown only for stationary settings.