Wearable BCI Hits 79.38% Online Decoding Accuracy

Summary The study was published in ITM Web of Conferences on August 14, 2026, addressing the demand for portable, real-time brain-computer interface systems in stroke rehabilitation by completing the physical integration and online experimental validation of a wearable system. The system uses a specialized EEG headset with miniaturized acquisition circuits secured via pogo pins, featuring 10 core recording channels positioned over the sensorimotor cortex. During the evaluation phase, the research team recruited 6 healthy subjects and 2 stroke-affected hemiplegic patients for closed-loop experiments based on motor imagery and motor attempts. Common Spatial Pattern was used for spatial feature extraction and Linear Discriminant Analysis for intention classification, with personalized sub-band optimization applied to further improve recognition. The authors report an average offline recognition rate of 84.91% and a classification accuracy of 79.38% in the more challenging online real-time testing. Analysis of spatiotemporal spectra and R² value distributions validated activation patterns in the sensorimotor areas during motor intention triggering, which the authors present as support for advancing the technology from laboratory settings toward community rehabilitation.