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

Training accurate BCI models depends on large, diverse EEG datasets, but that data is usually locked inside individual hospitals and labs, stored in inconsistent formats and restricted by privacy rules, which makes it hard to share. TrustBCI brings three functions into one platform: turning raw BCI datasets into assets that can be exchanged, using incentives to keep institutions contributing data, and letting users run computations without access to the raw records. The platform demonstrates two workflows, controllable data synthesis, which generates new datasets, and privacy-preserving query, which returns only query results. That suggests BCI models could be trained jointly across institutions without first pooling the data in one place.

Researchers showed that seizure-prediction methods lose much of their accuracy in continuous use, with mean AUROC dropping from 90% offline to 57.9% online across 664.9 hours of intracranial EEG from 16 patients with drug-resistant epilepsy, because brain signals drift over time. An online evolutionary framework that keeps selecting and updating models on the data stream predicted every seizure in 75% of patients and missed only one in 87.5%. Reading only contacts in the epileptogenic network cut the required contacts by 79.12%. The team has released the dataset. The study is a preprint and has not been peer reviewed.

This review traces the evolution of P300 brain-computer interfaces from classic spellers to AI agents. The P300 is a positive brain potential that appears about 300 milliseconds after a rare stimulus, and systems use it to determine which character a user wants to select. According to the review, recent systems such as ChatBCI and MindChat, which pair P300 spellers with large language models, cut keystrokes by more than 62% and nearly triple communication speed. The author also proposes an end-to-end architecture: an EEG headset and a real-time CNN detector, topped by an AI-agent layer made up of an LLM planner and IoT control interfaces.

Shanghai-based StairMed has taken its wireless implantable brain-computer interface system into human evaluation for people with speech impairments. The prospective, single-arm trial (NCT07647315) actually began on September 1, 2026, is now recruiting, and plans to enroll 4 participants. Subjects will undergo surgical implantation and brain-control training, followed by an expected 12 months of follow-up recording adverse events, serious adverse events and device deficiencies, along with efficacy data on brain-controlled interaction and communication. Primary completion is expected on August 30, 2028, and the study as a whole on December 30, 2028. The registry record does not disclose the implant site, decoding method or detailed eligibility criteria, and no interim results have been posted, so conclusions on safety and efficacy will have to wait for follow-up data.

Electrodes for non-invasive brain-computer interfaces have long traded off conductivity, conformity to the skin and durability. A conductive hydrogel made by in situ directional freezing copolymerization forms vertically aligned ion channels and an interconnected nanoporous network, giving it higher conductivity than conventional hydrogel electrodes. In preliminary P300 and steady-state visual evoked potential (SSVEP) experiments, it recorded signals comparable in quality to conventional wet- and dry-electrode benchmarks, while also offering a tissue-matched compressive modulus, high stretchability, skin adhesion and rapid self-healing. The paper presents these experiments as a feasibility demonstration and reports no data on long-term wear stability, scaled-up fabrication or human clinical trials.

Training a decoder to read brain signals usually means collecting a large labeled dataset from every new subject, which is slow and a burden on patients. The proposed method, MAPA, first runs self-supervised pretraining on unlabeled intracranial EEG recordings pooled across subjects, then transfers to new ones. The difficulty is that electrode contact placement and neuroanatomy vary from person to person, so MAPA adds two spatial encodings, an anatomical region embedding and a relative positional encoding, to a standard masked autoencoder. In cross-subject tests, about 164 labeled trials were enough to reach the accuracy that otherwise takes 3,500. The team reports that MAPA set new best results on the Neuroprobe benchmark in all three settings, within-session, cross-session and cross-subject, without fine-tuning, suggesting that calibration for implanted BCIs could become much shorter.

Because the fingers' representations sit close together in the motor cortex, telling individual finger movements apart from scalp EEG has long been difficult. Researchers first learned a set of spectral projections from simultaneously recorded EEG and functional MRI (fMRI), then used the class geometry derived from fMRI to correct EEG predictions. In tests on 12 healthy participants, group average accuracy for two-class movement execution rose from 66.93% to 74.53%, and for three-class execution from 44.83% to 56.58%; for two-class motor imagery it rose from 80.78% to 85.63%. At inference the system uses EEG alone, with no paired fMRI data required.