fNIRS

Researchers from Wuyi University and partner institutions proposed a method that combines EEG and functional near-infrared spectroscopy signals, reporting mean accuracy above the best baselines by 2.34, 0.59 and 1.47 percentage points on public-dataset tasks involving mental arithmetic, motor imagery and emotion recognition, respectively. The latter two differences were not statistically significant. The method separates information shared by the two signal types from information specific to each, then combines these representations for prediction. It addresses redundant multimodal information and difficulties in capturing temporal and spatial relationships. The authors interpret the findings as evidence of improved hybrid BCI decoding and strong generalization. These public-dataset results do not establish real-time performance or clinical effectiveness.

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.

A collaboration involving UC Berkeley, USC and Microsoft Research introduced MtReNIRS, pretrained on 12 public fNIRS datasets covering 268 subjects and about 567 recording hours, then evaluated on three unseen datasets. It aligns probe layouts for cross-subject transfer and within-subject calibration with two to eight labels per class. Cross-subject results were not uniformly significantly better than fully supervised baselines, and few-shot gains varied by dataset. Device effects were not isolated because device, task and montage differences co-occurred.

Hybrid motor-imagery brain-computer interfaces combine EEG with functional near-infrared spectroscopy (fNIRS) to exploit complementary electrical and blood-flow signals, but wearing two headsets is cumbersome and costly, and earlier diffusion-based methods for generating one signal from the other need about a thousand iterative steps, too slow for real-time use. Researchers from the Key Laboratory of Modern Teaching Technology (Ministry of Education) at Shaanxi Normal University and Microsoft (Beijing) propose Bio-MF, which predicts fNIRS waveforms directly from EEG in a single step. On one RTX PRO 6000 GPU it produces a trial in 7 ms, 857 times faster than a 1,000-step diffusion model. Adding the synthetic fNIRS to EEG raised classification accuracy over EEG alone by 3.37 to 4.15 percentage points on one dataset, and by 2.50 to 2.98 points on a second dataset with an unseen 64-channel EEG layout. All validation was offline; the work is a preprint and has not been peer reviewed.

About 70% to 80% of stroke survivors cannot live independently because of physical impairment, and regaining the ability to walk is central to changing that. A study registered by the First Affiliated Hospital of the University of Science and Technology of China, in Hefei in eastern China, plans to enroll 200 stroke patients and 200 healthy controls, recording EEG, fNIRS, sEMG, fMRI and other central and peripheral physiological signals alongside clinical measures such as facial action units, the Berg Balance Scale and Fugl-Meyer motor assessment. The goal is a non-invasive BCI dataset for stroke rehabilitation research. The cross-sectional observational study involves no intervention, and first enrollment is set for October 1, 2026.

ANT Neuro, the Dutch EEG equipment maker headquartered in Hengelo, announced a strategic collaboration on September 1, 2026 with optohive AG, the Swiss developer of the HiveOne functional near-infrared spectroscopy system. Under the agreement, HiveOne will be sold and supported through ANT Neuro's regional sales and technical teams, reaching the research customers the company says it has served for nearly 30 years. The two will also co-develop a hybrid cap that holds ANT Neuro's eego EEG technology and HiveOne together and synchronizes both data streams through Lab Streaming Layer; optohive was founded in 2025 as an ETH Zurich spin-off, the announcement said.

Researchers have released an open, near whole-head functional near-infrared spectroscopy (fNIRS) dataset from 57 participants and found that signal quality partly explains why brain activation differs from person to person. fNIRS, a non-invasive technique that shines light through the scalp to measure changes in blood oxygen in the brain, often yields results that vary widely between individuals and are hard to reproduce, and researchers suspect systemic physiological fluctuations and differences in signal quality may distort task-evoked hemodynamic responses. The team recorded participants at rest and during real movement, imagined movement, emotion recognition, visual and auditory tasks, alongside peripheral physiology including heart rate, respiration, skin conductance, ECG and EMG, plus short-channel recordings. Four quality metrics, including the scalp coupling index and signal-to-noise ratio, were all significantly related to channel length and to task activation estimates; group-level analysis confirmed activation in the expected brain regions. The study is a preprint and has not been peer reviewed.

EEG signals are only microvolts strong, so blinks, jaw clenching and muscle activity easily contaminate them. Researchers at Tianjin University and Tiangong University in northern China proposed MTGNet, a framework that suppresses these artifacts while preserving the information downstream tasks need. On the public EEGDenoiseNet dataset, it cut spectral relative root-mean-square error by 18.9%, 31.5% and 14.0% for EMG, EOG and mixed artifacts respectively; on a real-world fatigue EEG dataset, it raised classification accuracy by 6.20 to 6.69 percentage points over unprocessed input. Adapting the framework to a new task takes only 0.33 million low-rank adaptation (LoRA) parameters and no paired clean EEG reference.

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.