Papers

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 prospective single-centre study involving Columbia University Medical Center found that 13 of 41 clinically unresponsive, critically ill patients with acute brain injury (32%) could control a brain-computer interface. The system analyzed EEG in real time and translated attempted movement into changes in sound. Clinical consciousness returned by discharge in 4/13 patients with BCI control versus 1/28 without it. At 12 months, 6/13 versus 2/28 met the study's definition of functional independence. BCI control was associated with subsequent recovery, but the findings do not show that device use caused recovery. The tested interface was one-way; bidirectional communication remains a future possibility.

Authors at the University of California, San Francisco and UC Berkeley discuss how continuous closed-loop reinforcement interacts with sensory feedback when people learn to control human-machine interfaces. The Neuron commentary covers BCIs, augmented reality devices and hand prostheses.

A University at Buffalo author evaluated an assistive communication interface combining a BCI with eye tracking. Gaze narrows the target area and brain signals confirm selection. The study reports faster selection than BCI alone, better control than eye tracking alone, and fewer selection errors and corrections.

Authors at Mexico’s National Rehabilitation Institute and the Center for Research and Advanced Studies of the National Polytechnic Institute reviewed BCI cognitive rehabilitation. Studies report cognitive improvements in mild cognitive impairment, stroke and ADHD, but randomized trials are few and all reviewed applications use non-invasive EEG.

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.

Researchers developed a non-invasive EEG brain-computer interface combining auditory input with visual feedback to support phonological processing relevant to developmental dyslexia, then tested it first in adults with and without dyslexia. Participants completed 16 sessions and underwent pre- and post-training assessments of phonological awareness, single-word reading and non-word reading. Among participants who learned the BCI, offline learning scores were significantly associated with non-word reading gains in both groups. BCI learning was also significantly associated with improvements in real-word reading and amplitude rise time discrimination. These associations do not establish that training caused the gains or demonstrate benefits in infants or children.

Researchers built a control framework, the Intention Matrix, that classifies brain-wave signals to infer a user's intended leg movement and fuses that with data from the exoskeleton's own sensors to generate low-level control commands. Intention reading begins after the user initiates the limb movement. The authors say the results show preliminary feasibility for supporting the EXOS-1 exoskeleton in therapy; the abstract reports no participant numbers or accuracy figures.

A meta-analysis of 26 randomized controlled trials covering 1100 stroke patients found that brain-computer interface (BCI) rehabilitation improved upper-extremity motor function compared with control interventions, but showed no significant benefit for lower-limb function or walking tests. A dose-response analysis suggested an inverted U-shaped relationship, with the peak effect at approximately 48 min per session.

Can watching movements help stroke survivors retrain their arms? This scoping review screened 386 records and included 22 studies of upper-limb action observation therapy that measured EEG. Mu and beta rhythms over sensorimotor areas consistently desynchronized while patients watched movements, a sign that motor-related brain regions were engaged, and multi-week training produced sustained increases that often coincided with better upper-limb function scores. The authors see promise in BCI-based closed-loop therapy that uses EEG to adjust training in real time, but note that widely varying protocols make results hard to compare across studies.

A review of brain-computer interface (BCI) research in children finds preliminary evidence that non-invasive systems are feasible for those above 5 years of age, but the supporting studies are small and short. It compares five paradigms (P300, motor imagery, SSVEP, fNIRS and hybrid systems) and four applications: communication aids, motor rehabilitation, ADHD attention training and autism social-emotional intervention. Evidence is firmer for communication and motor rehabilitation in cerebral palsy than for ADHD and autism.

A network meta-analysis of 22 randomized study cohorts with 727 participants compared BCI-coupled robotic rehabilitation, robot-assisted therapy alone, motor imagery and conventional rehabilitation for arm weakness after stroke. BCI-coupled robotic rehabilitation ranked highest on upper-limb motor function scores, averaging 6.80 points above conventional rehabilitation, a point estimate above the 5.25-point minimal clinically important difference, although the lower confidence limit fell short of it. Its advantage over robot-assisted therapy alone was only 2.04 points, which the authors judged unlikely to be clinically important.

A brain-computer interface that times a nerve stimulus to the moment the brain prepares a wrist movement raised the excitability of the motor pathway from cortex to muscle in 17 able-bodied volunteers, whether they actually moved the wrist or only imagined it. Gains persisted 30 minutes after the session, and the two approaches did not differ significantly. The authors say this gives clinicians flexibility for patients who cannot move, though the study did not include stroke patients and individual responses varied widely.

A systematic review of 32 studies asks whether stroke survivors can retrain paralyzed limbs by imagining movement. Most approaches had patients imagine an action, read the signal from scalp EEG, and used it to drive virtual reality, functional electrical stimulation or robotic exoskeletons, closing the feedback loop. Training ranged from small pilots to as many as 80 sessions, averaging about 30. The authors conclude such methods can significantly improve clinical outcomes in stroke survivors, though the benefit depends on the specific modality and stroke characteristics, and there is still no agreed treatment dose.

Spanish researchers asked 15 healthy participants to imagine raising an arm while watching a humanoid robot do it, viewed in immersive virtual reality, on 2D video or live in person, and recorded their brain activity with EEG. Immersive VR produced the strongest activation of the sensorimotor cortex, measured as desynchronization, a weakening of EEG rhythms that indicates a more active region. The approach of engaging motor areas by watching and imagining, without moving, underlies rehabilitation for people with limited or lost mobility, and the researchers say immersive VR could help optimize such programs and brain-computer interface applications. The study included only healthy people and has not been tested in patients.

Researchers in Portugal had seven people with chronic stroke imagine arm movements while an EEG-based brain-computer interface gave some of them real-time feedback and others sham feedback; both groups also received four weeks of standard physiotherapy. Patients receiving real feedback improved beyond the minimal clinically important difference on at least one functional measure, most clearly grip strength, and their brain activity became more symmetrical between hemispheres. As a small case series, the study shows the method is feasible, and its results are preliminary.

Researchers divided 90 learners into two groups: one used a BCI platform that read attention, cognitive load and mental fatigue in real time and adjusted content and pacing accordingly; the other followed a conventional online course. The BCI group showed stronger sustained engagement and better knowledge retention. The mixed-methods design combined quantitative data (pre- and post-test scores, task completion rates and neural activity indicators) with surveys and semi-structured interviews on perceived engagement, usability and the overall learning experience. The participants came from English House Language Center, the European University of Armenia and Mesrop Mashtots University. The authors also flag unresolved privacy, ethical and accessibility questions around collecting and using neural data in education.

Researchers developed a non-invasive EEG brain-computer interface using auditory inputs and visual feedback to target phonological processing in developmental dyslexia, initially testing it in adults with and without dyslexia. Participants completed 16 sessions and underwent phonological awareness and reading assessments before and after training. Among participants who learned the BCI, including both dyslexic and control participants, offline BCI scores were significantly associated with improvements in nonword reading. BCI learning also showed significant associations with gains in real-word reading and amplitude rise time discrimination. These associations do not establish causation or demonstrate benefits in infants or children. This study is a preprint and has not been peer reviewed.

A brain-computer interface can read neural activity tied to movement, while a powered exoskeleton supplies the force needed to carry it out. But EEG is noisy, muscle signals shift with fatigue and recovery, and the right level of assistance depends heavily on the user and the task, so AI's main value lies in handling several changing signals at once rather than relying on a single fixed input. The review surveys how EEG, EMG and mechanical sensing are combined for exoskeleton control and contrasts two applications with very different goals: stroke rehabilitation and healthy users. The author argues that higher classification accuracy alone is not enough, since latency, calibration, fatigue, uncertainty and physical safety also determine whether a system is truly usable.

The MINTS protocol plans to enroll 21 people who are at least 6 months post-stroke and have persistent upper-limb motor impairments. They will complete 14 home-based motor imagery neurofeedback sessions, imagining movements while receiving feedback on brain activity. Training will use mobile EEG every second day. The single-group study will repeat measurements during baseline periods of different lengths and throughout training to track brain activity and motor performance. Primary assessments concern EEG activity and a measure of functional connectivity, alongside brain imaging, motor testing, daily-life movement monitoring, and an exploratory three-month follow-up. This protocol has not been peer reviewed and reports no training outcomes.

Electrodes for non-invasive brain-computer interfaces have long traded off conductivity, conformity to the skin and durability. A conductive hydrogel made by in situ directional freezing copolymerization forms vertically aligned ion channels and an interconnected nanoporous network, giving it higher conductivity than conventional hydrogel electrodes. In preliminary P300 and steady-state visual evoked potential (SSVEP) experiments, it recorded signals comparable in quality to conventional wet- and dry-electrode benchmarks, while also offering a tissue-matched compressive modulus, high stretchability, skin adhesion and rapid self-healing. The paper presents these experiments as a feasibility demonstration and reports no data on long-term wear stability, scaled-up fabrication or human clinical trials.