EEG

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.

Neurosom announced it has been granted a patent for a method that combines Bayesian statistical modeling with personalized EEG data and brain atlas models to achieve "super-resolution" in locating the origins of brain electrical activity, potentially improving the guidance of transcranial electrical stimulation (TES). The patent, titled "Method for Bayesian Super-Resolution of Electroencephalographic Source Analysis and Transcranial Electrical Stimulation" (US Pat. No. 12,663,866), was announced on July 9, 2026.

NIRx Medical Technologies and Artinis Medical Systems, the Dutch fNIRS pioneer, have formed a strategic partnership backed by healthcare investor Gilde Healthcare to speed innovation and broader adoption of functional near-infrared spectroscopy (fNIRS) brain imaging. Both companies keep their own brands and operating locations while combining R&D and commercial scale, and leadership is unchanged, with Dr. Patrick Britz remaining chief executive of NIRx and Dr. Willy Colier chief executive of Artinis.

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.

The Chinese Association of Rehabilitation Medicine will hold a workshop on EEG-driven closed-loop TMS in Guangzhou, southern China, on July 17, 2026, staged under its Naoke China (脑客中国) series and run by its committee on brain function monitoring and modulation in rehabilitation. The technique reads EEG in real time and triggers a TMS pulse when a target brain state appears, a 'brain-state-dependent stimulation' approach that pairs the spatial precision of TMS with the temporal resolution of EEG, and the program is built around moving BCI work out of the laboratory into neurorehabilitation and the precision diagnosis of brain disorders. Shenzhen Yingchi Technology, a subsidiary of Hanix United, is among the supporting organizations, and registration closes on July 17.

The FDA cleared Universal Brain's UB ERP System on June 30, 2026, finding it substantially equivalent under submission number K253767 through the traditional 510(k) pathway. The agency classifies it as Non-Normalizing Quantitative Electroencephalograph Software, a Class II device under regulation 882.1400, with neurology as the assigned review panel. Event-related potentials are obtained by averaging EEG time-locked to stimuli; components such as P300 serve both clinical assessment and the classic paradigms behind noninvasive BCI spellers.

The FDA cleared the Ceribell Monitor on June 30, 2026, finding it substantially equivalent under submission number K261101 through the traditional 510(k) pathway. The agency classifies it as a Reduced-Montage Standard Electroencephalograph, a Class II device under regulation 882.1400, with neurology as the assigned review panel. It is one of several Ceribell clearances in the same period; the product line is built around rapid reduced-montage setup for bedside recording in emergency and critical care.

MVCLDG combines raw EEG and Hilbert-derived phase information with domain-alignment and contrastive-learning constraints to improve classification across unseen users. It outperformed baseline and representative domain-generalization methods on a public error-related-negativity dataset and a semantic-syntactic-violation dataset without target-domain adaptation; ablation and activation-map analyses supported the contribution and neurophysiological plausibility of its components.

The FDA cleared the Ceribell Neurology Review Software on June 24, 2026, finding it substantially equivalent under submission number K260998 through the traditional 510(k) pathway. The agency classifies it as Automatic Event Detection Software for Full-Montage Electroencephalograph, a Class II device under regulation 882.1400, with neurology as the assigned review panel. The category covers software that automatically flags events within full-montage EEG recordings, surfacing candidate segments from long records for clinician review.

The FDA cleared Neuroelectrics Barcelona's Enobio Dx line, in 8-, 20- and 32-channel models, on June 13, 2026, finding it substantially equivalent under submission number K261604 through the special 510(k) pathway. The agency classifies it as a Full-Montage Standard Electroencephalograph, a Class II device under regulation 882.1400, with neurology as the assigned review panel. Neuroelectrics is known in neuromodulation and BCI research for noninvasive EEG acquisition and transcranial electrical stimulation hardware.

The FDA cleared VoxNeuro's Cognitive Function Neuroimaging (cfNI) Software, version 1.0.0, on June 12, 2026, finding it substantially equivalent under submission number K253015 through the traditional 510(k) pathway. The agency classifies it as Normalizing Quantitative Electroencephalograph Software, a Class II device under regulation 882.1400, with neurology as the assigned review panel. Normalizing quantitative EEG software compares an individual's EEG metrics against a reference population database before reporting, supporting objective assessment of cognitive function.

Researchers proposed ResDSNet, a residual depthwise-separable network with a matching preprocessing module that filters noise while keeping motor imagery features, to break what they call the "depth barrier": deepening lightweight decoders fails when residual structures are poorly designed or mismatched with preprocessing. On 3 public datasets covering 127 subjects it reached 79.36%, 84.95% and 64.13% accuracy, 3.16%, 1.59% and 8.40% higher than the best existing methods. It was tested only offline on public data, not in real-time BCI systems or patients.

Burning mouth syndrome produces months or years of burning oral pain with no visible lesion on examination, and treatment options are limited, leaving patients shuttling between oral medicine and psychiatry. A randomized, sham-controlled interventional trial registered by Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, will apply transcranial direct current stimulation over the left dorsolateral prefrontal cortex while collecting resting-state brain function and structural imaging, tracking pain, anxiety, depression, sleep and salivary flow. The trial is funded by the hospital's Brain-Computer Interface Special Research Program and registered as ChiCTR2600124987 on May 20, 2026. It plans to enroll 38 participants, 19 in the active group and 19 in the sham group, with first enrollment set for June 1, 2026; recruitment has not yet started.

Researchers have built a hybrid batch-stream framework for EEG-based emotion recognition, which until now has had to choose between accurate but slow offline batch processing and fast but less accurate real-time streaming. The system estimates whether the streaming module is currently reliable from its past performance, then decides probabilistically whether to use the batch or the streaming output. It achieved classification accuracies of 85%, 94% and 74% on the public DEAP, AMIGOS and SEED benchmarks, and was also tested under concept drift, when data distributions shift over time. Results come from offline evaluation on public datasets and have not been validated with real users.

Shandong Provincial Hospital in eastern China has registered a pilot trial that will test whether patients with acute ischemic stroke can begin brain-signal-driven arm training within days of onset. The planned 60 participants will practice upper-limb movements with a scalp EEG brain-computer interface that reads their intention to move and gives training feedback, compared against a sham-training group. Both groups will train for 10 days on top of standard early rehabilitation, twice a day for about 30 minutes per session. The primary outcome is the change in upper-limb motor function score 30 days after randomization. The trial is not yet recruiting and is expected to start in October 2026.

Researchers in Chongqing, a municipality in southwestern China, have proposed SPD-DANN, a method that aims to decode motor imagery, the brain signals produced when a person imagines moving a hand or foot, in new users without per-person calibration. Because EEG varies between people and drifts over time, BCIs typically need fresh data from each new user. SPD-DANN represents EEG features on a symmetric positive definite (SPD) matrix manifold, a mathematical space that better fits their nonlinear structure, and uses adversarial learning to extract features shared across individuals. Tested on 4 BCI datasets, the method outperformed several existing unsupervised domain adaptation techniques, the researchers said. The abstract gives no accuracy figures, and all tests were offline.

The Chinese Clinical Trial Registry (ChiCTR) has logged a multicenter randomized controlled trial testing whether brain-computer interface-based mindfulness meditation relieves the symptoms of chronic tinnitus. Registered on April 23, 2026 as ChiCTR2600123258, the trial has not begun enrolling patients, and its design, sample size and sponsor must be read from the original registration record. It is the first multicenter registration to combine BCI with mindfulness meditation for chronic tinnitus, moving the approach from single-center exploration toward multicenter validation.

A brain-computer interface (BCI) trial for upper-limb motor rehabilitation after hemorrhagic stroke was registered with the Chinese Clinical Trial Registry under the number ChiCTR2600122859 on April 20, 2026, and has not yet begun recruiting. The study combines electroencephalography (EEG) with functional near-infrared spectroscopy (fNIRS). The registration record does not disclose the study design, sample size or sponsor.

Researchers at Xidian University built DisCo-Former, a Transformer framework for single-trial rapid serial visual presentation (RSVP) EEG decoding with three components guided by neurophysiological priors, then found its attention consistently collapsed: attention maps went nearly uniform and value-projection weights shrank toward 0. Stripping out the Transformer encoder left DisCo-MLP, a pure multilayer perceptron that matched or beat the Transformer version across two datasets and three evaluation regimes, with within-subject mean AUCs of about 0.94 to 0.98. For RSVP-EEG, the authors argue, modeling the signal's structure matters more than architectural complexity. The study was published in the International Journal of Neural Systems on April 10, 2026.

The FDA cleared Zeto's New Wave System on March 13, 2026, finding it substantially equivalent under submission number K260455 through the special 510(k) pathway. The agency classifies it as a Full-Montage Standard Electroencephalograph, a Class II device under regulation 882.1400, with neurology as the assigned review panel. That category covers standard electroencephalograph hardware acquiring a complete montage of scalp channels.

A Chinese trial will measure the immediate effects of a non-invasive brain-machine interface combined with multimodal training on neurophysiological function in patients with upper-limb hemiparesis after stroke. It was registered with the Chinese Clinical Trial Registry as ChiCTR2600120046 on March 9, 2026 and has not begun recruiting.

Researchers built STAND-Net, a spiking neural network that cleans blink, muscle and other artifacts from EEG and computes only when the signal changes, combining a leaky integrate-and-fire encoder-decoder, a dilated-convolution residual backbone and an attention mechanism that locates artifacts from neuron firing patterns. Against state-of-the-art methods it raised signal-to-distortion ratio by more than 3.7 dB across artifact types and used 97.98% less power than comparable deep neural networks; BCI classification on the cleaned signals was 6.64% more accurate. The results come from the paper's abstract, with no data from wearers.

Researchers have proposed MSARFNet, a multi-scale attention-based reconstruction fusion network that reached average classification accuracies of 84.64% and 87.96% on the BCI Competition IV 2a and 2b motor imagery datasets, outperforming several existing methods. The network extracts spatio-temporal features through parallel multi-scale convolutional branches and fuses them with an attention mechanism to sharpen transient motor-imagery responses, targeting the non-stationary EEG signals and inter-subject variability that make MI decoding unreliable. The study was published on February 27, 2026, in IEEE Journal of Biomedical and Health Informatics.

The FDA cleared Clouds of Care's PreOp v3 on February 13, 2026, finding it substantially equivalent under submission number K252565 through the special 510(k) pathway. The agency classifies it as Source Localization Software for Electroencephalograph or Magnetoencephalograph, a Class II device under regulation 882.1400, with neurology as the assigned review panel. Source localization software infers the intracranial origin of signals from scalp-recorded electric or magnetic fields, a step in neurosurgical planning such as presurgical localization of epileptic foci.

Researchers designed a paradigm in which the same visual stimulus elicits five cognitive processes (reaching, intercepting, reaching in sequence, reaching after attending, and withholding a reach) and distinguished them from scalp EEG with 91.72% ± 6.10% accuracy, using a regularized linear discriminant analysis classifier with event-related spectral perturbation features. In the sequence reach task, temporal features separated normal from catch trials with 77.96% ± 7.03% accuracy. The analysis was offline; the team plans to improve decoding and extend it to online use.

Researchers have proposed MSHANet, a deep learning model that reads imagined limb movements from scalp EEG, a signal often used in rehabilitation after stroke or spinal cord injury. The paper notes that motor imagery EEG is nonlinear and varies widely between people, which limits decoding accuracy. The model combines spatiotemporal feature extraction, interactive-head self-attention, dynamic squeeze-and-excitation attention and a temporal convolutional network. On three public datasets it reached within-subject accuracies of 83.56%, 89.75% and 75.66%, and cross-subject accuracies of 69.93%, 81.85% and 79.67%. A separate encoding method that writes electrode positions into the input raised within- and cross-subject accuracy on BCI-2a by 2.83% and 2.91%. All results come from offline public data and have not been validated in real time on patients.