Carnegie Mellon University

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 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.