Papers

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.

Researchers discuss how recent BCI advances in electrode longevity and biocompatibility could address key olfactory implant challenges.

Researchers in Germany, the Netherlands and Japan argue in an opinion piece that brain-computer interface design should be rebuilt around ideomotor theory, which treats voluntary action as driven by internally represented sensory outcomes. BCI research has made remarkable technical progress but remains limited in scope, the authors write, typically relying on motor and visual cortex signals in a narrow range of patient populations, and they describe this underused framework as a principled basis for next-generation interfaces that align more closely with the brain's own intentional and action-planning architecture. Reorganizing BCIs around the purpose of an action, meaning the user's goals and anticipated effects, would be a more intuitive, generalizable and scalable path, they suggest, and advances in neural recording and artificial intelligence-based decoding of sensory representations make the shift feasible and timely, potentially easing persistent usability and generalizability problems in BCI design.

Researchers designed a paradigm in which the same visual stimulus elicits five cognitive processes (reaching, intercepting, reaching in sequence, reaching after attending, and withholding a reach) and distinguished them from scalp EEG with 91.72% ± 6.10% accuracy, using a regularized linear discriminant analysis classifier with event-related spectral perturbation features. In the sequence reach task, temporal features separated normal from catch trials with 77.96% ± 7.03% accuracy. The analysis was offline; the team plans to improve decoding and extend it to online use.

Researchers have proposed MSHANet, a deep learning model that reads imagined limb movements from scalp EEG, a signal often used in rehabilitation after stroke or spinal cord injury. The paper notes that motor imagery EEG is nonlinear and varies widely between people, which limits decoding accuracy. The model combines spatiotemporal feature extraction, interactive-head self-attention, dynamic squeeze-and-excitation attention and a temporal convolutional network. On three public datasets it reached within-subject accuracies of 83.56%, 89.75% and 75.66%, and cross-subject accuracies of 69.93%, 81.85% and 79.67%. A separate encoding method that writes electrode positions into the input raised within- and cross-subject accuracy on BCI-2a by 2.83% and 2.91%. All results come from offline public data and have not been validated in real time on patients.

A study published in January 2026 proposes RUNet, a motor imagery EEG decoding framework designed to skip the calibration session BCIs usually need before each use, since EEG varies across sessions and between people because of individual and environmental factors. RUNet is first pre-trained without labels on resting-state EEG to learn session- and subject-invariant features, then retrained on task data, using Riemannian manifold mappings to suppress cross-session and cross-subject drift. It reached an average cross-session accuracy of 87.19% and a cross-subject accuracy of 68.09% on BCI Competition IV 2a, and a cross-subject accuracy of 78.14% on PhysioNet. All results come from offline dataset tests and have not been validated in real-time use.

A Neuron paper describes Neuropixels Ultra, which packs 6,144 switchable recording sites at 6-micrometer center-to-center spacing while reading 384 channels simultaneously. In mouse visual cortex recordings, neuronal yield increased by more than twofold. The study also improved subcellular signal detection and cell-type classification, but it did not report simultaneous recording from 10,000 neurons.

The study proposes a discriminative, geometry-aware dimensionality reduction method on the Riemannian manifold for symmetric positive definite (SPD) matrices in EEG classification, aimed at boosting discrimination while reducing information loss. On BCI Competition IV Dataset 1 and Dataset 2a, the method improved classification accuracy by 5.0% and 19.38% respectively, indicating robust performance is retained after dimensionality reduction.