ArticleFrontiers in artificial intelligence2026
Cognitive load gating system in motor imagery BCIs: a dual-task EEG study with differential entropy-based reliability estimation.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Brain-computer interface (BCI) systems based on motor imagery hold significant clinical value for individuals who have lost voluntary movement, but most studies test BCI assistive devices like powered wheelchairs under ideal conditions where motor imagery signals are not interfered by simultaneous cognitive load. This work records electroencephalography (EEG) from 13 participants across four tasks: baseline rest, mental arithmetic (Easy, Medium, Hard), pure left/right motor imagery and both tasks simultaneously, to build a two-layer classification system. The first layer decodes motor intent using a model chosen from nine classifiers, from Filter Bank Common Spatial Pattern (FBCSP) and Riemannian geometry families. The second layer is a subject-specific safety gate that combines the classifier's decision-margin confidence score with 39-dimensional Differential Entropy (DE) features extracted from the theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequency bands across all 13 electrodes, feeding a logistic regression boundary to predict trial-level MI prediction reliability. FBCSP + SVM-Linear model emerged as the best-performing model on pure motor imagery cross-validation. Under 10-fold cross-validated pure motor imagery, the mean balanced accuracy across all subjects was 0.594 and degraded to 0.517 on dual-task trials, a statistically significant reduction (Wilcoxon W = 14.0,
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