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

Researchers at the University of Washington have built mechanically compliant, precision-porous brain implants and tested them in rat brains. At 4 weeks, the porous scaffolds drew less astrocyte encapsulation than solid hydrogel rods, softer hydrogels reduced pro-inflammatory macrophage polarization, and new blood vessels, neuronal markers and neurogenesis appeared inside the pores. The authors present the design as a route to limiting glial scarring and improving regeneration in implant-based central nervous system therapies.

US researchers using intracortical microelectrode arrays identified distinct neural patterns in the motor and somatosensory cortex of patients with spinal cord injury that separately encode the timing and force of grasping, offering new ideas for developing more precise brain-computer interface (BCI) systems.

The Center for Neurotechnology listed nine new papers spanning experimental and computational neuroscience, neural interfaces and neuroethics. The selection includes Smart Dura for multimodal neural recording and modulation, work on transcutaneous spinal-stimulation trials, primate optogenetics, co-adaptive interfaces, motor-cortex activity and participatory neuroethics research.

The University of Aveiro in Portugal has registered a study (NCT07732868) testing a brain-machine interface protocol that pairs virtual reality with sensory feedback, visual, auditory and tactile, and/or an exoskeleton in people with spinal cord injury. Participants attend 12 monitored sessions, one a week, each lasting roughly one to two hours, moving a virtual avatar through motor imagery while non-invasive EEG records brain activity. The study focuses on changes in brain activity that track clinical improvement and on how information moves between brain regions, and it collects age, sex, injury level and type, time since injury, functional grade and neuropathic pain; it is at the registration stage with no results yet.

Researchers in Pakistan and South Korea have built EmoWrite, a brain-computer interface that uses sentiment analysis to turn thought into text, typing at 6.6 words per minute with 90.36% accuracy in tests with 72 volunteers. The system adds a dynamic keyboard and visual feedback to make the interface easier to use.

Researchers writing in npj Biomedical Innovations have proposed DSTF-Net, a framework that decodes steady-state visual evoked potentials (SSVEP) from frontal EEG alone, removing the need for electrodes over the occipital cortex. In cross-subject transfer to 20 new users, including eight brain-injured patients lying supine, it improved decoding accuracy by as much as 33.47% over baseline methods.

Researchers have designed a wearable readout chip that records EEG, EOG, EMG and ECG and transmits multichannel data at 10 Mbps using body channel communication, which uses the wearer's body as the transmission medium. The chip alternates between recording and transmitting in separate time slots, so noise from the transmitter does not contaminate faint brain signals, and dropping the bias electrode raised communication signal amplitude about 2.13-fold. Fabricated in a 180-nm process, it reached a total common-mode rejection ratio of 101.1 dB and stayed stable under 18-Vpp common-mode interference and 15% electrode mismatch. The work targets wearable human-machine interfaces such as extended reality and BCIs; the abstract reports chip test metrics, not results from BCI use.

Nanjing has built end-to-end competitiveness across the brain-computer interface industry chain, People's Daily Online reported on July 28, 2026. According to the report, the city earlier issued an action plan to become a national hub for BCI industry innovation and has set up a 3+3+3 BCI industry innovation system, drawing on universities including Southeast University and Nanjing University of Aeronautics and Astronautics to build 3 provincial- and ministerial-level key laboratories, and landing three core platforms covering neural signal coding and decoding, dedicated chips and brain imaging. The 2026 national BCI conference was also held in Nanjing. The report summed up the city's strengths as research, medical resources, application scenarios and capital ecosystem.

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.