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Researchers have found that whether people can learn to control a device with their thoughts may depend on whether the control scheme follows the brain's own activity patterns. Participants lying in an MRI scanner controlled a video game avatar in real time by modulating activity in brain regions responsible for spatial navigation. When the researchers then changed the mapping between brain activity and avatar movement, participants could readjust and regain control if the new mapping fell along directions in which their brain activity already varied widely, known as the intrinsic manifold; when the mapping departed from that structure, they never learned it. The researchers say the principle could be used to design BCIs that are easier to learn.

A study published in Computers in biology and medicine applies cross-frequency bispectral analysis to nonlinear EEG activity during the planning and execution of natural reach-to-grasp movements. The researchers extracted magnitude- and phase-based features from complex bicoherence matrices and assessed them through classification, permutation-based feature selection, and within-subject statistics. Execution showed stronger nonlinear coupling than planning, led mainly by beta- and gamma-driven interactions. Decoding precision versus power grasps performed similarly across the two stages, suggesting that grasp-type representations emerge during planning and persist into execution. Compared with conventional analytical baselines, bispectral features provided consistent advantages for grasp-type discrimination and multiclass classification. The findings offer a new set of motor-decoding features for future brain-computer interface and neuroprosthetic research.

The Chinese Clinical Trial Registry (ChiCTR) logged a new study on June 5, 2026 under the number ChiCTR2600126291, testing whether a period of multisensory embodied training beforehand alters the neural representation of imagined movement and the decoding performance of a brain-computer interface (BCI). Recruitment has not yet begun.

A newly registered trial tests a brain-computer interface in post-stroke rehabilitation across 4 parallel arms: full cross-domain (emotion-cognition-motor) adaptive training, integrated cognitive and motor task training, single-dimension motor training, and conventional rehabilitation. Each arm plans 22 participants, 88 in total. It enrolls patients aged 30 to 80 with a first cerebral infarction or hemorrhage 3 months to 1 year earlier, unilateral hemiplegia, and upper-limb Brunnstrom stage II to IV. The primary outcome is a motor function score; secondary outcomes include EEG motor imagery event-related synchronization/desynchronization, prefrontal theta/alpha power ratio, and EEG-fNIRS coupling.

Women with overactive bladder often rely on drugs and behavioral training, with limited relief. A newly registered trial at The First Affiliated Hospital of Soochow University takes a different route: patients perform a task while functional near-infrared spectroscopy reads prefrontal brain activity, and an algorithm decides when to deliver transcranial magnetic stimulation, closing the loop. The interventional, parallel-design trial plans to enroll 66 women aged 18 to 75, 33 in the experimental arm and 33 in a control arm that receives open-loop sham stimulation, with the overactive bladder symptom score as the primary endpoint. No device model or results are disclosed in the registration, and recruitment has not yet started.

The Chinese Clinical Trial Registry (ChiCTR) has logged a study of how brain-computer interface training built on a motor imagery paradigm affects lower-limb motor function in stroke patients. Registered on May 28, 2026 as ChiCTR2600125562, the trial is recruiting; design, sample size and sponsor are absent from the record carried by the WHO International Clinical Trials Registry Platform (ICTRP) and must be read from the original ChiCTR entry.

The randomized controlled trial plans to enroll 60 patients with lower limb motor impairment after stroke, with the intervention group receiving BCI-assisted exoskeleton rehabilitation training and the control group conventional walking training. Primary outcomes include lower limb surface electromyography, lower limb motor function, the Modified Barthel Index, a balance scale and functional ambulation, testing whether a BCI-driven exoskeleton adds functional benefit over conventional gait training. Punan Hospital, Pudong New Area, Shanghai is the sponsor, with first enrolment set for May 30, 2026. The trial is registered with the Chinese Clinical Trial Registry (ChiCTR) under number ChiCTR2600125531, with a registration date of May 28, 2026, and recruitment has not yet started.

The General Office of China's Tianjin Municipal People's Government issued the Action Plan for Promoting Innovative Development of Brain-Computer Interfaces in Tianjin (2026-2030), document number Jin Zheng Ban Fa [2026] No. 8, dated May 26, 2026 and released on May 28. All product targets sit on the non-invasive track, while invasive systems are covered only by milestones for entering registration trials and winning market approval. By 2027 Tianjin wants two to three national-level innovation platforms, more than five non-invasive products in clinical use, more than ten new products across elderly care, education, entertainment, smart living and advanced manufacturing, 50 innovative firms, one to two national standards, and three to five industry funds. By 2030 it targets more than ten non-invasive products in large-scale clinical use, market approval for invasive products, and a fund cluster exceeding 10 billion yuan.

The randomized controlled trial plans to enroll 58 patients with upper limb motor dysfunction after stroke. The test group will receive feedback-based rehabilitation training through brain-computer interface interaction, while the control group will undergo traditional upper limb circular motion training. The primary outcome is the Fugl-Meyer Motor Assessment-Upper Extremity (FMA-UE), with secondary outcomes covering motor function, daily living activities, quality of life and neurophysiological measures including EEG, transcranial magnetic stimulation and fNIRS. The trial was prospectively registered, with first enrolment set for June 1, 2026, and recruitment has not yet started.

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.