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China's brain-computer interface sector recorded 47 funding rounds in the first half of 2026, 38 of them disclosing terms for a combined RMB 5.38 billion (approx. $750 million). A broader tally used by Chinese financial media puts the total at 64 deals and roughly RMB 7.25 billion (approx. $1 billion). Either figure is more than double the whole of 2025 (49 deals, RMB 2.72 billion) at a comparable deal count. The record lists every deal by month — company, technical approach, round, amount, investors — and identifies three structural patterns: ultrasound and non-invasive approaches moving quickly into a field long defined by implants, Big Tech and municipal state funds entering at the same time, and nearly half of all transactions still sitting at angel or seed stage.

A multicenter cohort study aims to enroll 1,000 stroke patients, with the treatment arm receiving closed-loop BCI plus functional electrical stimulation rehabilitation and the control arm receiving conventional training. The primary endpoint is 2-week change in upper and lower limb FMA scores. First enrollment is scheduled for September 1, 2026.

The preprint was posted to arXiv on August 13, 2026, proposing a hybrid deep-learning architecture that combines a convolutional neural network with a bidirectional long short-term memory network to decode motor-imagery EEG. The author notes that motor-imagery brain-computer interfaces are seen as a promising route to flexible communication between the brain and external devices, particularly for people affected by stroke or neurodegenerative disorders, but that reliable decoding remains difficult because EEG recordings carry substantial noise and relate to underlying brain activity in complex, weakly informative ways. In the proposed architecture, the CNN learns high-level spatial and temporal representations directly from raw MI-EEG recordings, while the bi-LSTM models temporal dependencies among the extracted features. The approach was evaluated on both a publicly available dataset and a privately acquired dataset collected with an EEG acquisition system, with robust performance reported on two- and three-class motor-imagery classification and promising subject-independent decoding across the methods compared. The work is a preprint and has not been peer reviewed.

The study was published in Journal of neural engineering on August 13, 2026, presenting the first strength-duration characterization of cortical evoked potentials elicited by endovascular stimulation adjacent to the cerebellum. The authors note that electrical stimulation and neural recording underpin neural prostheses for restoring function and treating neurological disorders, but that clinical adoption is limited by the invasiveness of implantation, while the Endovascular Neural Interface offers an alternative by accessing intracranial targets through the cerebral vasculature. A polymer-based stent-electrode array was deployed into the left transverse sinus of an ovine model, and biphasic current pulses targeting the cerebellum were delivered via the stent electrodes while a subdural electrocorticography grid recorded cortical responses. Endovascular stimulation consistently evoked time-locked cortical potentials with early and late components at approximately 40 ms and 100 ms post-stimulation, and impedance monitoring confirmed electrode functionality and stability throughout. Strength-duration analysis revealed rheobase and chronaxie values, providing a quantitative basis for parameter selection and comparison with established intracranial stimulation modalities.

A prospective cohort of 1,641 motor dysfunction patients, 66.24% chronic stroke, provides the first clinical profile for personalised BCI system design.

A completed RCT compared BCI-controlled versus conventional exoskeleton gait training in stroke patients, training 2 times a week for 4 weeks.

A new preprint introduces BLPM, an EEG-language foundation model that reframes EEG decoding as continuous semantic embedding prediction.