Carnegie Mellon University

Researchers from the University of Pittsburgh and Carnegie Mellon University tested whether changes in attentional load affected intracortical BCI control in two participants with tetraplegia. Participants moved and clicked a 2D cursor while completing a working memory task. EEG measures and difficulty ratings suggested increased attentional load during dual-tasking, but BCI performance remained largely stable across the dual-task conditions. P2 showed small but significant increases in trial completion time and normalized path length under mild compared with moderate load. The authors suggested that differences in signal quality between participants may have influenced the results; this remains an interpretation rather than an established causal relationship.

A participant with severe upper-limb paralysis caused by amyotrophic lateral sclerosis (ALS) wore an endovascular stent-electrode array and a scalp EEG cap at the same time, in the same session, while attempting ankle flexion and extension. Both recordings changed markedly during attempted movement, but the stent array showed stronger per-channel motor modulation and was unaffected by skull attenuation. Scalp EEG was more susceptible to eye blinks and jaw-muscle activity, while the stent array picked up prominent cardiac signals. Neither method reliably distinguished left from right ankle movement, leaving spatial localization an open problem.

A workshop at the BCI Society Meeting 2025, run with the Implantable BCI Collaborative Community (iBCI-CC), took up how clinical outcome assessments (COAs) for pivotal BCI trials should be selected, developed and validated. Participants pointed to patient heterogeneity, the absence of widely validated COAs, and the difficulty of capturing outcomes that matter in home and daily-life settings. The discussion lays groundwork for the iBCI-CC Clinical Study Endpoints Workgroup to build a transparent process for identifying meaningful aspects of health and concepts of interest, in support of regulatory approval and reimbursement.

Carnegie Mellon University researchers built an intracortical brain-computer interface that lets animals use visual neurofeedback to hold prefrontal cortex population activity near a preset target. Arousal drifts during tasks, with low arousal linked to drowsiness and high arousal to stress, and these departures from optimal performance show up as fluctuations in neural activity. With moment-by-moment feedback, the animals reduced those fluctuations on timescales from seconds down to hundreds of milliseconds, and arousal-related regulation of neural activity was associated with BCI use. The team says neurofeedback could help strengthen or restore regulation of neural activity in clinical conditions where arousal control is impaired. The work is a preprint, has not been peer reviewed and has been done only in animals.

Carnegie Mellon University researchers report a sensory-guided joint learning framework that pairs human motor learning with adaptive machine learning to train motor imagery BCI users. Across 31 BCI-naive participants, average online discrete accuracy was 86.0% in one dimension and 77.5% in two, with continuous control accuracy at 77.5% and 66.9% respectively; tactile guidance reduced how much users had to explore and accelerated neural adaptation, while sample reweighting kept decoder updates aligned with the learner's own trajectory. The authors frame the approach as a shift from passive calibration to active human-machine joint learning; the study appears in Nature Communications.