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A bioRxiv preprint finds that adding a surprisal predictor on top of GPT-2 contextual embeddings contributes almost nothing extra to predicting high-gamma ECoG responses during speech comprehension.

Researchers at the Center for Neurotechnology at the University of Washington recorded from two male monkeys with high-density laminar microelectrode arrays and found that reaching-related activity in frontal motor cortex is unevenly distributed both across the cortical surface and with depth. Target-direction information varied sharply between neural populations, but the amount of task information a population carried predicted which populations shared similar temporal dynamics. The authors say the pattern should inform where electrodes are placed in future brain-computer interface implants.

A study of 20 healthy participants who trained self-regulation of four cortical rhythms — frontal midline theta, occipital alpha, unilateral central-temporal sensorimotor rhythm, and central beta — across four neurofeedback sessions found that all could regulate at least two features, but none could regulate frontal midline theta. The authors argue this shows 'non-learners' is not a personal trait that generalizes across features, informing future neurofeedback and BCI training protocol design.

An early feasibility study followed five people with spinal cord injury, each implanted with two Blackrock NeuroPort arrays in somatosensory cortex. Across two to 10 years of implantation, more than 168 million intracortical stimulation pulses were delivered over 27 combined implant-years without a serious adverse event or direct harm to electrode health. An average of 64% ± 13% of electrodes still evoked touch, including 60% after 10 years in one participant.

Sun Yat-sen University researchers propose a minimally invasive hybrid BCI using 300-800 um skull micro-holes for distributed microelectrodes; rat experiments show improved SNR over scalp EEG. Preprint; not yet peer-reviewed.

Researchers decoded 15 semantic categories from sEEG high-gamma activity in epilepsy patients at 29.8% accuracy (chance 6.7%), supporting semantic decoding feasibility for language BCIs. Preprint; not yet peer-reviewed.

Carnegie Mellon University researchers report a sensory-guided joint learning framework that pairs human motor learning with adaptive machine learning to train motor imagery BCI users. Across 31 BCI-naive participants, average online discrete accuracy was 86.0% in one dimension and 77.5% in two, with continuous control accuracy at 77.5% and 66.9% respectively; tactile guidance reduced how much users had to explore and accelerated neural adaptation, while sample reweighting kept decoder updates aligned with the learner's own trajectory. The authors frame the approach as a shift from passive calibration to active human-machine joint learning; the study appears in Nature Communications.

Researchers have proposed MOJO, a training scheme that pairs masked autoencoding with a supervised objective for decoders that tokenize spiking activity. Tested on monkey motor cortex, multi-region mouse recordings and human electrocorticography during speech, MOJO beat purely supervised models, with the widest margins in few-shot finetuning where labels were limited, and produced more interpretable neuronal representations. The paper, posted to arXiv on July 15, 2026, has not been peer reviewed.