BCIwiki — Brain-computer interface news, research and industry database

Would drivers accept a car that reads their brain signals and steps in when they are overloaded? In an online experiment, 1201 participants each viewed 1 of 12 animated driving scenarios. In each scenario, a wearable, non-invasive brain-computer interface detected high workload and triggered assistance. Acceptance varied systematically across scenarios. It was higher when the system supported driving-related functions and when the driver shown had a disability. The 2 automation activation modes tested made no difference.

A study combines EEG data with multilingual text to classify emotions, since EEG alone is prone to noise while language models read sentiment in words but miss implicit changes in the brain. The author reports 89.7% accuracy for the fused model, versus 75% for EEG alone and 79% for text alone, with an F1-score of 0.88 and an average inference latency of 180 ms, enough for real-time recognition. The abstract does not state the number of participants or the datasets used.

Researchers have proposed CortexBridge, a lightweight adapter that lets EEG foundation models, usually pretrained on a single fixed electrode layout, handle any montage. It combines EEG features with electrode and brain-atlas coordinates to map signals into a shared cortical latent space before passing them to the pretrained model, which is left unchanged. On 5 BCI datasets from the public MOABB benchmark, tested with 3 frozen foundation models, EEGPT, LaBraM and CBraMod, it improved performance in 13 of 15 evaluations, raising balanced accuracy by an average of 0.80%, 0.70% and 3.26% respectively, with a maximum gain of 13.02% on 12-class steady-state visually evoked potential classification. The study is a preprint and has not been peer reviewed.

Researchers have used a generative adversarial network, an AI model that can produce synthetic data, to generate artificial primary motor cortex signals from other functionally related cortical regions and add them to training data, addressing the shortage of task-relevant signals in any single subject's recordings. On MEG data recorded while subjects performed reaching tasks, motor decoding performance rose by about 10% (p < 0.05) after augmentation, and the gain held even without real primary motor cortex signals. The method was also applied to a motor imagery BCI competition dataset to improve classification accuracy.

Researchers combined high-resolution surface electrophysiology recordings from the first 70 patients implanted with an anatomically conformal, thin-film neural interface to build a functional atlas of the human central nervous system. The interface was positioned at more than 100 locations, with >100,000 electrode deployments spanning motor, sensory, language and other functional regions, as well as the brainstem and spinal cord. Aggregated recordings showed spatially ordered digit representations, movement-related frequency-band changes and task-modulated neural population activity. The study also demonstrated real-time neuroprosthetic control and decoding of cortical activity associated with speech, facial movements and emotional expressions. This approach combines recordings across patients rather than simultaneously reading the entire central nervous system in one person. This study is a preprint and has not been peer reviewed.

Researchers implanted an intracortical brain-computer interface in a participant with complete tetraplegia that reads movement intent from motor cortex to drive muscle electrical stimulation for grasping, while hand-mounted force sensors send touch information back by stimulating the sensory cortex. A separate channel pairs spinal cord stimulation with sensory input to promote neural plasticity. The participant could self-feed and pick up fragile objects. Improvements in elbow flexion strength and wrist sensation persisted after stimulation stopped, suggesting the system may be retraining neural pathways rather than only replacing them.

A research team has used a single high-density electrocorticography (ECoG) implant, an electrode array placed on the brain's surface, to decode speech and gestures simultaneously in people with paralysis, ApplySci reported. When participants simply attempted to speak or gesture, a personalized virtual avatar spoke or moved accordingly, either both at once or separately. The researchers expected the brain signals during combined expression to be a simple sum of the two, but the data differed clearly from speech or gestures alone. Participants practiced a limited vocabulary of up to 10 gestures and 10 words or phrases.