China

Researchers from Guangxi University, Wuming Hospital of Guangxi Medical University, Tsinghua University and partner institutions developed a skin-mimetic gradient electrode, softer at the scalp and firmer at the circuit connection, with graded pores for sustained electrolyte release. Two male participants completed an 8-hour daily-activity recording session and a 14-hour session, respectively. In the latter, hourly SSVEP input tasks maintained accuracy above 95%, and mVEP tasks above 90%. Each long-duration session involved one participant; the results do not establish population-wide performance or clinical efficacy.

Researchers from Kunming University of Science and Technology and collaborating institutions proposed a theoretical system for basic BCI research, formalizing BCIs as closed-loop probabilistic information-processing models through information theory and statistical decision-making. The work addresses system decodability, performance boundaries and dynamic constraints. It organizes scientific hypotheses, neuroscience principles, fundamental theorems and basic laws, derives four results that can guide experimental design, and uses motor imagery BCIs to illustrate application boundaries. The authors aim to provide a unified analytical benchmark and support a shift from experience-driven algorithm optimization toward theory-driven system analysis and design.

Researchers at Kunming University of Science and Technology reviewed the mechanisms and outcome evaluation of neurofeedback for post-traumatic stress disorder. They compared EEG, real-time functional magnetic resonance imaging and functional near-infrared spectroscopy in feedback targets, spatiotemporal resolution and clinical applicability. Neurofeedback provides real-time information about brain activity to guide patients in regulating abnormal neural patterns. Using clinical scales, neurophysiological measures and functional recovery indicators, the review discusses its clinical position relative to first-line treatments and potential synergy with psychological interventions. The authors identify inadequate control designs and non-standardized training parameters as ongoing limitations; therapeutic potential should not be treated as established efficacy.

Researchers from Beijing University of Technology and collaborating institutions proposed REFS to select EEG features for multi-dimensional emotion recognition when some emotional labels are missing. The method reconstructs incomplete labels while reducing redundancy among selected features, addressing modeling challenges posed by high-dimensional features and relatively few high-quality samples. Tests used DREAMER, DEAP and HDED, three datasets with different EEG channel configurations, at varying label-missing ratios. REFS consistently outperformed the 13 advanced feature-selection methods compared in robustness under these conditions. These dataset findings do not establish performance in deployed affective BCIs.

A patient with chronic complete spinal cord injury improved from AIS A to AIS C and regained partial voluntary movement after one year of combined neuromodulation and conventional rehabilitation. The 53-year-old man had a T9 injury and received sequential spinal cord electrical stimulation, BCI training, motor cortex stimulation, and peripheral magnetic stimulation of both iliopsoas muscles, alongside 12 months of rehabilitation. His left lower limb motor score rose from 0 to 6, spontaneous defecation and penile erection returned, and his neuropathic pain score fell from 8 to 0. This uncontrolled case suggests a possible association with functional recovery but cannot distinguish the contributions of individual interventions or exclude spontaneous neurological recovery.

A collaboration involving West China Hospital of Sichuan University combined healthy neural recordings with biophysical constraints to decode motor signals affected by pathology. In a gesture classification task using high-density electrocorticography from patients with gliomas infiltrating the motor cortex, the study reported improved classification accuracy. Correlation between motor-command representations and healthy canonical representations increased from r = 0.66 to r = 0.94. The authors interpret the model's behavior as an "inverse weighting" strategy that downweights high-power pathological noise and redirects spatial attention toward functional compensatory regions. This is a proof of concept for decoding in compromised networks, not evidence of restored motor function.

A review traces implantable BCI substrates from rigid materials such as silicon to bendable but inextensible polymers and then intrinsically stretchable elastomers. It focuses on the mechanical mismatch between microelectrodes and soft brain tissue, which can cause persistent injury and foreign body responses that compromise long-term use. The authors highlight electrospun nanofibrous microelectrodes, which combine stretchability with an interconnected, highly permeable three-dimensional network. They argue that this structure helps address the metabolic limitations of dense films. The review also discusses high-density manufacturing, biochemical modulation, and untethered integration rather than presenting human efficacy testing of a new device.

Neuracle Medical Technology (Shanghai) Co., Ltd. listed its Model 1014-35 cortical electrode in the National Medical Products Administration's medical device unique identification database. The entry identifies the electrode as a component of an implanted EEG electrode kit, which in turn forms part of an implanted BCI hand motor function compensation system. The system also includes a BCI implant, an EEG transceiver, pneumatic glove equipment, and related software. The entry clarifies these component relationships; UDI listing itself is neither a new marketing authorization nor evidence of clinical effectiveness.

Neuracle Medical Technology (Shanghai) Co., Ltd. listed an Implanted EEG Electrode Kit, Model K1014-25, in the National Medical Products Administration's medical device unique identification database. The entry describes a kit containing a cortical electrode (1014-25) and supporting components, forming part of an implanted BCI hand motor function compensation system. The system also includes a BCI implant, an EEG transceiver, pneumatic glove equipment, and related software. The record identifies the kit and its relationship to the system; UDI listing itself is neither a new marketing authorization nor evidence of functional improvement in patients.

Neuracle Medical Technology (Shanghai) Co., Ltd. listed its Model 1014-25 cortical electrode in the National Medical Products Administration's medical device unique identification database. The entry identifies the electrode as part of an implantable EEG electrode kit, which is a component of an implantable BCI hand motor function compensation system. The full system also includes a BCI implant, an EEG transceiver, pneumatic glove equipment, and related software. The record identifies the electrode and its relationship to the kit and system; UDI listing itself is neither a new marketing authorization nor clinical evidence of improved hand function.

Neuracle Medical Technology (Shanghai) Co., Ltd. listed its K1014-35 Implantable EEG Electrode Kit in the National Medical Products Administration's medical device unique identification database. The entry describes a kit containing a cortical electrode (1014-35) and supporting components, forming part of an implantable BCI hand motor function compensation system. The complete system also includes a BCI implant, an EEG transceiver, pneumatic glove equipment, and related software. The record identifies the kit and its relationship to the system; UDI listing itself is neither a new marketing authorization nor evidence of safety or functional improvement in patients.

Neuracle Medical Technology (Shanghai) Co., Ltd. listed its Model 1014-15 cortical electrode in the National Medical Products Administration's medical device unique identification database. The entry identifies the electrode as a component of an implantable EEG electrode kit, which forms part of an implantable BCI hand motor function compensation system. The system also includes a BCI implant, an EEG transceiver, pneumatic glove equipment, and related software. The record specifies the electrode's model, identifier, and relationship to the system; UDI listing itself is neither a new marketing authorization nor clinical evidence of improved hand function.

Neuracle Medical Technology (Shanghai) Co., Ltd. listed its K1014-15 Implantable EEG Electrode Kit in the National Medical Products Administration's medical device unique identification database. The entry describes a kit containing a cortical electrode (1014-15) and supporting components, forming part of an implantable BCI hand motor function compensation system. The complete system also includes a BCI implant, an EEG transceiver, pneumatic glove equipment, and related software. The record identifies the kit and its relationship to the system; UDI listing itself is neither a new marketing authorization nor evidence of safety or improved hand function in patients.

Researchers at Xi’an Jiaotong University and collaborating institutions developed E-STACNet to model spatial relationships between EEG channels alongside changes over time. Mean accuracies were 88.00% on BCI Competition IV 2a and 80.62% on Giga DB; DEAP valence and arousal classification reached 96.49% and 96.46%.

China’s NMPA UDI database lists Neuracle’s NEO-M and NEO-M Pro hand motor function compensation system models under registration certificate 国械注准20263120537, and K1014-25 and K1014-35 implantable EEG electrode kits under 国械注准20263120536. The descriptions identify the system’s implantable components, EEG transceiver, pneumatic glove and software.

Motor imagery brain-computer interfaces decode neural signals associated with imagined movements to enable communication between the brain and external devices. A review surveys applications in stroke, spinal cord injury, and virtual reality rehabilitation, focusing on integration with EEG, neurofeedback, functional electrical stimulation, robotic devices, and artificial intelligence. It identifies weak signals relative to noise, substantial individual differences, and lengthy training as continuing challenges. The author highlights deep learning, wearable devices, personalized rehabilitation programs, and clinical validation as development directions. The article reviews technologies and applications rather than reporting a new clinical efficacy trial.

A perspective paper proposes designing neural implants as functional tissues made of living cells, rather than solely as electrodes connecting to nerves. Called Cybertissue Neuroprosthetics, the concept aims for structural and functional integration with the host nervous system to support long-term neuromodulation and information exchange. It optionally incorporates minimal nanoelectronics, nanomaterials, or genetic modifications while seeking to reduce foreign materials and use cellular signaling mechanisms. The author proposes this approach to address interface deterioration during long-term implantation, but lifetime integration remains a goal, not an established device capability or clinical outcome.

Researchers at Beijing Sport University developed a motor imagery EEG decoding model and evaluated it offline on three public datasets using subject-specific and cross-subject protocols. Against the baseline with the highest mean accuracy, subject-specific gains on two datasets were 3.56 and 1.62 percentage points; another cross-subject comparison showed a 2.08-point difference. These three comparisons had statistical support after correction within their respective settings, but none remained significant under a broader correction covering 56 comparisons. The findings offer limited support for integrating local and longer-range EEG information in stages, rather than establishing benefits for online control or clinical use.

Researchers developed a soft monitoring system that conforms to the brain surface and validated it in rabbit brain tumour excision models. It measures contact pressure during surgery while recording neural electrical signals and temperature. The study found a correlation between somatosensory evoked potentials, a type of neural electrical signal, and pressure-induced damage to cortical neural pathways. Evans blue staining and magnetic resonance imaging showed that contact pressure above 80 kPa caused irreversible craniocerebral injury under the experimental conditions studied. The system uses a temperature-responsive hydrogel interface. The pressure finding comes from animal experiments and does not establish a safety threshold for human surgery.

Brain-computer interfaces (BCIs) face human factors bottlenecks such as reliability and usability as they move toward real-world use, yet human factors engineering has mostly studied overt behavior and peripheral physiology rather than brain activity as a control input. This conceptual review proposes Human Factors Engineering for BCIs as an interdisciplinary direction, centered on user neurocognitive traits, brain-computer co-adaptation, multimodal feedback and context, with 3 core scientific questions and ethics, privacy and user rights as an overarching dimension.

EEG signals vary between people and drift over time, so a brain-computer interface usually has to be recalibrated for each new user. Researchers at Hangzhou Dianzi University built TSMD-Net, which learns from several existing subjects without any data from the new user, then pools what it learned about each one into a single classifier. It reached mean accuracies of 70.21%, 77.82% and 79.53% on three public EEG datasets.

When a user looks at targets flickering at different frequencies, the visual cortex produces EEG responses at matching frequencies, known as steady-state visual evoked potentials (SSVEP), which a brain-computer interface can use to infer the intended target. Such systems usually need calibration data, and more data means more user fatigue. Researchers propose SCR-FTSDNN, a deep convolutional network that moves EEG into the frequency domain, then extracts temporal and spatial features with added spatial and channel reconstruction modules. Tested on the public Benchmark and BETA datasets, the authors report higher recognition accuracy and stability than several existing methods, especially with limited calibration data. The abstract gives no specific figures, and the results come from offline tests.

The First Affiliated Hospital of Bengbu Medical University, in eastern China's Anhui province, is recruiting for a clinical trial that uses brain-computer interface (BCI) neurofeedback to treat the cognitive-control deficits that keep people with internet gaming disorder from stopping play. The system reads frontal midline theta rhythms and feeds them back in real time so participants learn to regulate their own brain activity, with social rewards built in as reinforcers to improve efficacy and adherence. The trial plans to enroll 120 patients across three arms: dual-target neurofeedback plus social reward, neurofeedback alone, and sham feedback. Primary completion is expected on December 30, 2028.

A collaboration involving Peking University converted dynamic natural movie frames into spatiotemporal patterns of optogenetic stimulation in awake mice, then reconstructed movie frames from responses in the primary visual cortex. Comparing natural viewing with artificial stimulation, they found that stimulation evoked broadly comparable cortical dynamics and aligned representations. A deep learning model reconstructed frames from cortical signals under both conditions. The animal study offers a proof of concept for evaluating how visual cortical prostheses might translate images into stimulation patterns. It does not establish that stimulation produced the corresponding visual experience in mice or restored vision in humans.