ECoG

A participant with severe upper-limb paralysis caused by amyotrophic lateral sclerosis (ALS) wore an endovascular stent-electrode array and a scalp EEG cap at the same time, in the same session, while attempting ankle flexion and extension. Both recordings changed markedly during attempted movement, but the stent array showed stronger per-channel motor modulation and was unaffected by skull attenuation. Scalp EEG was more susceptible to eye blinks and jaw-muscle activity, while the stent array picked up prominent cardiac signals. Neither method reliably distinguished left from right ankle movement, leaving spatial localization an open problem.

Intracranial EEG (iEEG), recorded by electrodes implanted inside the brain, is widely regarded as an ideal signal for decoding intent, but differing datasets and preprocessing pipelines make it hard to tell whether models are actually improving. A research team built iMINDBench, a benchmark that brings together naturalistic movie-watching data from three institutions, 15 decoding tasks, standardized preprocessing and fixed evaluation splits. Pretrained systems generally beat baselines within their own preprocessing pipeline, but classic spectral baselines remained competitive on other institutions' data. Scaling up supervised data from other subjects or institutions to 25 times the volume yielded only limited, task-dependent gains over training on data from the same session.

The study was published in Journal of neural engineering on August 13, 2026, presenting the first strength-duration characterization of cortical evoked potentials elicited by endovascular stimulation adjacent to the cerebellum. The authors note that electrical stimulation and neural recording underpin neural prostheses for restoring function and treating neurological disorders, but that clinical adoption is limited by the invasiveness of implantation, while the Endovascular Neural Interface offers an alternative by accessing intracranial targets through the cerebral vasculature. A polymer-based stent-electrode array was deployed into the left transverse sinus of an ovine model, and biphasic current pulses targeting the cerebellum were delivered via the stent electrodes while a subdural electrocorticography grid recorded cortical responses. Endovascular stimulation consistently evoked time-locked cortical potentials with early and late components at approximately 40 ms and 100 ms post-stimulation, and impedance monitoring confirmed electrode functionality and stability throughout. Strength-duration analysis revealed rheobase and chronaxie values, providing a quantitative basis for parameter selection and comparison with established intracranial stimulation modalities.

Published in Journal of Neural Engineering on August 7, 2026, the study analyzed human electrocorticography data from four subjects to investigate medial wall contributions to finger movement decoding. Significantly above-chance finger movement detection was observed across multiple medial wall subregions, with local motor potentials and oscillatory power in the 8-12 Hz and 12-34 Hz bands contributing most strongly. Feature dynamics shared key properties with primary motor cortex, including pre-movement desynchronization, while also exhibiting region-specific positive or negative LMP modulations. Medial wall channels in two subjects enabled significant differentiation between individual fingers, and one subject showed decoding of both contralateral and ipsilateral finger movements, though this is a single case and preliminary.

Brown University researchers have posted a preprint proposing Neural SS-DMP, a movement decoder that does not output hand coordinates directly: it first infers a compact set of parameters describing the motion the user intends, then hands them to a generator governed by physical dynamics that draws the full trajectory, so decoded output stays within motion a body can actually produce. The generator is tuned per person, blending general movement dynamics with the individual's own patterns estimated from training data, and the authors say that across two kinds of neural recording the model came out ahead of strong existing methods on both accuracy and trajectory smoothness while holding performance longer on recordings made after training ended. The study is a preprint, has not been peer reviewed, and its results come entirely from offline data rather than live control.

The researchers developed a deep learning architecture that analyzes multichannel interictal intracranial EEG to localize epileptogenic zones without requiring seizure-period recordings or manual annotation. The model combines a Morlet-wavelet temporal Transformer with a spatial attention encoder. It was evaluated with 50.5 hours of recordings from 161 patients and 17,012 channels at 7 independent centers. Leave-one-center-out validation produced a pooled AUROC of 0.778 with a 95% confidence interval of 0.748 to 0.808, and discrimination remained above chance at every held-out center. The results indicate performance comparable to established electrophysiological baselines across centers and implantation modalities, but prospective clinical validation is still needed. This medRxiv preprint has not been peer reviewed.

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.

University of Washington Seattle researchers combine optogenetic stimulation with micro-ECoG in a semi-chronic implant, enabling stable causal connectivity measurement in macaques over weeks to years.

Researchers propose an injectable, in-situ-gelling multifunctional hydrogel to address the failure modes of micro-ECoG cortical recording — dural barrier disruption, cortical micromotion that weakens device-tissue coupling, and biofouling that triggers a foreign-body response. Combining dopamine-grafted sodium alginate with branched polyethyleneimine, the hydrogel forms a quasi-zwitterionic network that resists nonspecific protein adsorption and provides catechol-mediated wet adhesion, gelling rapidly under surgical-compatible conditions through dual macromolecular crosslinking without diffusible small-molecule monomers. Integrated with a 128-channel flexible micro-ECoG mesh array, the platform reduced glial activation and fibrotic encapsulation and preserved stable, high-fidelity cortical recording over the 3-week early chronic period. The authors say co-designing barrier repair, interface adhesion and antifouling in a single material can improve long-term function.

Published in Cell Reports Methods on June 12, 2026, the study presents high-density micro-electrocorticography arrays that integrate silicone elastomers (optical transparency, repeated penetration with intracortical arrays) and polyimide films (fine photolithographic feature definition) for multi-scale studies of brain activity. The combination facilitates high-throughput functional mapping to identify targets and insertion of intracortical arrays for dense local sampling. The authors demonstrated functional mapping in rats, cats and marmosets, guiding multi-area laminar recordings, and demonstrated local and feedforward optogenetic stimulation to investigate cortico-cortical interactions.

A Neurosurgical Focus paper tested Precision Neuroscience's 1,024-channel Layer 7 micro-ECoG array in four awake-craniotomy patients. Four-word speech classification reached 77.5% accuracy and four-direction cursor classification reached 78% to 84%, with no device-related adverse events reported during the procedures. The small, short intraoperative study was a feasibility test rather than a pivotal motor-restoration trial.