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Researchers have built a 32-channel event-based analog front-end chip in 180 nm CMOS that encodes biosignals in two modes, pulse frequency modulation and adaptive asynchronous delta modulation. The chip retunes its output data rate in real time to the envelope of the incoming signal, giving high compression and, the authors argue, a route to wireless transmission and online processing of neural signals in brain-computer interfaces.

A team writing in Computing and Informatics has proposed FSDFGL, a federated graph learning framework that folds EMG into EEG when the graph is built and then shares only structural information between clients, avoiding the accuracy loss that comes with exchanging heterogeneous features. The design targets three problems in hybrid BCI work at once: the low spatial resolution of EEG, datasets that are small and privacy-sensitive, and the uneven data distributions federated learning has to cope with. Experiments on the TAN dataset show an advantage in identifying complex motor intentions.

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 The study has not been peer reviewed.

Grenoble University Hospital has registered a clinical study (NCT07698496) assessing the feasibility and effectiveness of a chronic brain-computer interface for speech rehabilitation in people with locked-in syndrome (LIS), where non-invasive communication carries high cognitive load and existing invasive speech BCIs still rely on percutaneous connectors with infection risk. Using an intracranial epidural BCI paired with a speech synthesizer, the SpeechBCI protocol will test two complementary approaches in the same subject — a speech BCI (primary objective, the BCI_PAROLE device) and a cursor BCI (secondary), both running on the WIMAGINE intracranial epidural system. The trial is in the registration stage and has no clinical results yet.

A preprint proposes a two-stage CNN-LSTM-RL framework that uses reinforcement learning to apply residual kinematic corrections to the output of a CNN-LSTM continuous motor imagery decoder. The RL agent is trained offline, without direct EEG input, to optimize motion accuracy against a target trajectory. Compared with CNN-LSTM alone, CNN-LSTM-RL lifted the 2D mean correlation from 0.5076 to 0.7181 and the VR-scene correlation from 0.6420 to 0.7780, cutting RMSE by 40.2% and 38.2% respectively. The authors say correcting kinematic error through offline residual RL strengthens 3D BCI motor imagery decoding without additional neural data; the study has not been peer reviewed.

Chinese authorities recently issued a three-year action plan for the rehabilitation assistive-device industry that integrates brain-computer interface (BCI), artificial intelligence and flexible electronics with rehabilitation device development, and pursues medical-engineering collaboration to drive technical breakthroughs in materials and components.

University of Toronto researchers use diffusion latent representations as an intermediate layer for neural speech decoding, with word error rate varying sharply by timestep.

Nova Southeastern University researchers combine intracranial ECoG network features with a support vector machine and Bayesian updating for real-time continuous seizure risk forecasting.

Clinicians and researchers in Guangzhou, southern China, will get hands-on time with closed-loop systems that trigger transcranial magnetic stimulation from real-time EEG analysis at a workshop on July 17, 2026. Shenzhen Yingchi Technology, one of the supporting companies, will bring EEG-driven closed-loop TMS devices and near-infrared brain function imaging equipment. The session is organized by the Professional Committee on Brain Function Detection and Regulation Rehabilitation of the Chinese Association of Rehabilitation Medicine, alongside the committee's 2026 annual conference and the 8th Brain Function Detection and Regulation Rehabilitation Forum.