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

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 The study is a preprint, has not been peer reviewed, and its results come entirely from offline data rather than live control.

Researchers report a preprint, posted to arXiv on July 28, 2026 and not yet peer reviewed, on the methodological problem of eliciting explainable AI (XAI) requirements from stroke survivors. Existing protocols run dyadic interviews and overlook facilitation dynamics; this formative study moved to a survivor-caregiver triad, with three stroke survivors (two with moderate-to-severe aphasia) and three caregivers, and facilitators used four scaffolding techniques: analogical bridging, projective personas, binary forcing and extended response time. A reflexive analysis identified three systematic facilitation biases — normative bias, hypothesis confirmation bias and the presence effect — which the authors present as protocol risk guidelines for practitioners.

The second annual Neurotech Leaders Forum drew executives, investors, researchers and regulators from Europe and the United States to imec's campus in Leuven, Belgium, on June 23-24, 2026, for sessions on clinical translation, capital formation, regulatory strategy and emerging frontiers. Thomas Stieglitz of the University of Freiburg gave the Day 1 keynote, arguing for perseverance and functionalist design, and later sessions covered women's health, European BCI ventures and the region's capital and regulatory challenges. Companies showing new work included WISE SpA, with its Heron spinal cord stimulation lead, and Melora Medical, with a transcutaneous auricular vagus nerve stimulation (taVNS) device for dermatology.

Ecole Polytechnique Fédérale de Lausanne (EPFL) has launched a clinical study evaluating the preliminary safety and effectiveness of a cortical recording device (ECoG) combined with lumbar targeted epidural electrical stimulation (EES) to restore voluntary lower-limb motor function in participants with chronic spinal cord injury and mobility impairment. The study aims to establish a direct bridge between motor intention and the spinal cord below the lesion, potentially improving or restoring voluntary leg control and promoting neurological recovery when combined with neurorehabilitation.

Hospices Civils de Lyon has registered a clinical study in France to decode motor imagery from non-invasive brain recordings as a prerequisite for innovative motor rehabilitation therapies. Combining MRI, MEG, and EEG, the study will design a subject-specific neurophysiological model, noting that standard BCI approaches neglect transient features such as beta bursts. The approach will first be validated in healthy subjects, then assessed for feasibility in stroke patients.

A preprint proposes three regression models — a partial least squares regressor, a multilayer perceptron and an attention-based regressor — to decode multiple kinematic and kinetic parameters of grasp-and-lift tasks simultaneously from EEG signals. Evaluated on the WAY EEG GAL dataset, the attention-based regressor performed best with an R² of 0.8 and 29.2 ms latency, markedly improving simultaneous multi-parameter decoding, though per-parameter decoding declined; the multilayer perceptron was more consistent across the two settings but less accurate (R² = 0.49). The study has not been peer reviewed.

A preprint proposes EEGForceFusion, a hybrid EEG decoding framework that jointly models continuous and tokenized representations for grasp-force decoding, where continuous decoding is limited by complex temporal dynamics, high inter-subject variability and poor generalization. The framework combines convolutional-recurrent representation learning, quantized tokenization and Transformer-based temporal modeling in a unified fusion regression architecture to capture both fine-grained neural structure and long-range temporal dependencies. Under strict leave-one-subject-out cross-validation on the WAY-EEG-GAL dataset, it reached an offline R² of 0.817 and a simulated real-time R² of 0.793, with latency suited to real-time deployment. The study has not been peer reviewed.

A preprint proposes UnSPC (uncertainty-guided self-paced cyclic learning), a framework that integrates domain adaptation (DA) and domain generalization (DG) in an iterative cycle to address neural drift in long-term invasive brain-machine interfaces, where drift erodes decoding performance and forces frequent recalibration and where existing methods rely on DA or DG alone. Under an uncertainty-guided, self-paced pseudo-labeling scheme with a noise-robust ranking strategy, UnSPC iteratively mines reliable pseudo-labeled samples and, through cyclic adaptation and generalization, gradually mitigates both global and sub-domain drift. Experiments on multiple neural decoding datasets validated its effectiveness and robustness; the authors say it is the first method to integrate DA and DG through a pseudo-label loop. The study has not been peer reviewed.

The VA Office of Research and Development is advancing a high-performance implanted brain-computer interface (BCI) to improve independence for Veterans and others with tetraplegia or inability to speak due to ALS, spinal cord injury, or stroke. The project enhances deep learning decoders and multi-state gesture decoding, deployed on a battery-powered mobile BCI device for independent home use of computers and touch-enabled devices. Accuracy and usability will be evaluated in participants already enrolled in the BrainGate investigational clinical trial.