brain–computer interface
brain–computer interface
Overview
A brain–computer interface (BCI) is a system that enables direct communication between neural activity and an external device, bypassing conventional neuromuscular output. In biomedical research, BCIs are typically developed to decode signals from the brain and translate them into commands for assistive technologies, rehabilitation platforms, or experimental neuromodulation systems. They are most often studied in the context of neurological disease, motor impairment, and closed-loop neurotechnology.
BCIs are relevant to neuroscience because they link brain signal acquisition, signal processing, and device control in a single translational framework. Depending on the application, they may be combined with deep brain stimulation, neurorehabilitation, or other brain–gut axis and symptom-modulating interventions. Their clinical significance lies in their potential to restore function, support communication, and provide adaptive control in disorders such as Parkinson's disease and other conditions involving impaired motor or cognitive output.
Focus of Latest Publications
Recent publications involving brain–computer interface in this set did not evaluate the technology as an intervention or device; instead, they used it as a target term in studies centered on body mass index (BMI), obesity, diabetes, and related clinical outcomes. Across these reports, BMI was most often examined as a prognostic or predictive metric in retrospective cohorts, cross-sectional analyses, and feasibility or protocol studies, with outcomes ranging from postoperative metabolic trajectories to functional recovery, mortality, and measurement accuracy.
Several studies assessed BMI as a predictor of clinical outcomes in disease-specific settings. In rheumatoid arthritis, higher baseline BMI was associated with poorer early HAQ-DI-based functional recovery after first recorded advanced therapy, while the association with DAS28-ESR remission was weaker and model-sensitive. In mechanical thrombectomy for stroke, obesity was associated with comparable discharge functional outcomes to normal BMI, whereas underweight status was linked to worse discharge NIHSS scores and higher in-hospital mortality. In burn injury and chronic kidney disease, the abstracts framed BMI or related anthropometric indices as candidate prognostic markers for mortality risk, though no results were provided for those studies in the abstracts supplied.
Other publications focused on BMI in metabolic and cardiometabolic contexts. One study aimed to develop a machine-learning framework to predict postoperative BMI trajectories and long-term type 2 diabetes remission after metabolic bariatric surgery using preoperative data and time-dependent weight evolution. Another examined BMI in relation to mortality among adults aged 16–50 years with and without type 2 diabetes, and a separate cross-sectional analysis compared BMI, waist-to-height ratio, and visceral fat for predicting hypertension and diabetes. A mechanistic human study also reported that circulating sphingolipid profiles varied by BMI and glucose status, supporting sphingolipids as potential biomarkers for obesity, diabetes, and associated complications.
A smaller number of publications addressed BMI in measurement or intervention contexts. In obese surgical patients, BMI and other arm-related measures were poor classifiers of non-invasive blood pressure measurement error, despite frequent inaccuracy of cuff-based readings. A protocol described a weight-neutral health intervention for adults with BMI ≥30 kg/m², emphasizing feasibility and acceptability rather than efficacy. Another study evaluated a culinary medicine intervention in patients with type 2 diabetes and elevated BMI, but the abstract provided only the study design and did not report outcomes.