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Researchers designed an assistive Boccia ramp controlled by a brain-computer interface so that children with severe motor impairments who cannot speak can take part in the Paralympic sport. Six caregivers took part in semi-structured interviews and six children completed a newly developed 21-item survey; the children showed keen interest in playing Boccia with a BCI. The study also maps current barriers and facilitators to sport participation, and the issues that must be resolved before such technology can be rolled out.

Researchers propose a function predefined convolutional neural network (FPCNN) for decoding spontaneous EEG in brain-computer interfaces. Its learnable function predefined convolution (FPC) layer searches for the key spatial-frequency parameters of spontaneous EEG so the parameters carry clear physical meaning, and it builds trainable orthogonal detectors on the FPC to capture complex phase-varying signals. On three spontaneous EEG datasets, FPCNN outperformed state-of-the-art methods by 2.09%, 3.08% and 3.41%, with single-round training and testing taking just 67.96 and 19.36 seconds on non-GPU hardware, which the authors say makes it suited to EEG processing in diverse environments.

The study presents an EEG emotion recognition framework that couples discriminative channel selection with hierarchical spatial-temporal modeling. Wavelet coherence and mutual information adaptively select informative channels across frequency bands, and a spatial-temporal graph-aware network (STG-Net) models inter-channel spatial relations and the temporal evolution of emotional states before fusing frequency-spatial-temporal features for classification. The authors report better recognition accuracy and model efficiency than state-of-the-art methods.

The FDA cleared Clouds of Care's PreOp v3 on February 13, 2026, finding it substantially equivalent under submission number K252565 through the special 510(k) pathway. The agency classifies it as Source Localization Software for Electroencephalograph or Magnetoencephalograph, a Class II device under regulation 882.1400, with neurology as the assigned review panel. Source localization software infers the intracranial origin of signals from scalp-recorded electric or magnetic fields, a step in neurosurgical planning such as presurgical localization of epileptic foci.

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.

Neuralink said its clinical trials worldwide now include 21 participants. After making surgical and postoperative changes in response to thread retraction in the first recipient, the company reported higher signal quality in 18 of the next 20 participants. The figures come from a company update rather than a peer-reviewed clinical report.

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.