Germany

Running a motor-imagery BCI while transcranial alternating current stimulation (tACS) is switched on has been difficult, because stimulation artifacts swamp the brain rhythms the decoder relies on and push accuracy close to chance. In a controlled test with 14 healthy participants, a real-time spatial filtering pipeline combining spatio-spectral decomposition with beamforming held accuracy at 76 ± 9% during stimulation, while a standard Laplacian filter managed only 58 ± 9%. That makes closed-loop "stimulate while reading" operation feasible at the signal level; whether it actually improves BCI performance or produces neuroplastic changes, the paper explicitly leaves to future research.

ANT Neuro, the Dutch EEG equipment maker headquartered in Hengelo, announced a strategic collaboration on September 1, 2026 with optohive AG, the Swiss developer of the HiveOne functional near-infrared spectroscopy system. Under the agreement, HiveOne will be sold and supported through ANT Neuro's regional sales and technical teams, reaching the research customers the company says it has served for nearly 30 years. The two will also co-develop a hybrid cap that holds ANT Neuro's eego EEG technology and HiveOne together and synchronizes both data streams through Lab Streaming Layer; optohive was founded in 2025 as an ETH Zurich spin-off, the announcement said.

A review published on August 20, 2026 in npj Biomedical Innovations proposes brain-inspired brain-computer interfaces (BI-BCIs), a framework that unifies neuromorphic computing with BCI design to make neurotechnology lower-power, smaller and capable of closed-loop operation. The authors, from Aarhus University, Stanford University, the University of Southern Denmark, the University of Genoa, Forschungszentrum Jülich and RWTH Aachen University, say the approach could advance neuroprosthetics and neuromodulation for neurological disorders.

Researchers at the Institute of Biological Information Processing at Forschungszentrum Jülich posted a preprint to bioRxiv on August 4, 2026, proposing hierarchical, dendrite-inspired organic bioelectronic interfaces built to integrate with neurons. Brain-computer interfaces depend on intimate electrical communication between living neurons and artificial materials, the authors write, yet conventional electrode architectures remain structurally unlike neural tissue, limiting stable cell-electrode coupling and long-term recording. The work is a preprint and has not been peer reviewed.

A preprint proposes an event-based neural decoding method for neuroprosthetic motor control, arguing that deep-network-driven prostheses are limited by high latency, energy use and space — wired links restrict mobility and wireless links restrict information throughput, while spiking networks trade off task performance for low-power inference. The authors' event-based gated recurrent unit generates a sparse communication pattern with graded spikes, outperforming classical spiking neural networks on task performance, and pairs with efficient training and sparse inference to enable on-device neural decoding under latency, energy and space constraints. The study has not been peer reviewed.

Researchers combined electroencephalography (EEG)-based passive brain-computer interface (BCI) with eye tracking to decode users' interaction intent in real time during dynamic VR gameplay, reaching 69.64% accuracy in the online closed-loop phase.

Researchers recorded magnetoencephalography (MEG) from 18 right-handed participants as they imagined sounds and pictures, then compared two decoders: a convolutional neural network (CNN) and a linear logistic regression model. The CNN decoded both tasks above chance and exceeded 70% accuracy for visual imagery. It still decoded significantly when trained only on cortical regions unrelated to the task, suggesting imagined content is spread across partially overlapping networks rather than confined to a single sensory area, an experimental basis for feeding auditory and visual information into BCI decoders together.

Published in Cell Reports Methods on June 12, 2026, the study presents high-density micro-electrocorticography arrays that integrate silicone elastomers (optical transparency, repeated penetration with intracortical arrays) and polyimide films (fine photolithographic feature definition) for multi-scale studies of brain activity. The combination facilitates high-throughput functional mapping to identify targets and insertion of intracortical arrays for dense local sampling. The authors demonstrated functional mapping in rats, cats and marmosets, guiding multi-area laminar recordings, and demonstrated local and feedforward optogenetic stimulation to investigate cortico-cortical interactions.

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.

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.