Decoding sequence auditory expectations for clinical diagnostic and prognostic assessment of prolonged disorders of consciousness

OBJECTIVE: Electroencephalogram (EEG) features are important objective markers for diagnostic and prognostic evaluation of prolonged disorders of consciousness (DOC). However, task-based EEG paradigms are limited by mismatches between task demands and patients' residual awareness, as well as the limited individual assessment capacity of machine-learning-based detection frameworks. APPROACH: In this study, we proposed a lowdemand DOC detection strategy by decoding expectations of global regularities in sequence auditory stimulation using a modified local-global paradigm. Three single-trial classification frameworks using canonical correlation analysis, principal component analysis, and xDAWN for feature extraction were developed for individualized detection of global-regularity expectations. We further established awareness evaluation criteria based on single-trial classification outcomes and evaluated their diagnostic and prognostic effectiveness in a DOC experiment. MAIN RESULTS: Experiments in healthy participants demonstrated that the principal components of event-related potentials associated with global regularities were preserved under the proposed detection strategy. Compared with the original local-global paradigm, this strategy significantly improved single-trial classification performance for global effects by up to 15%. Electrophysiological analyses indicated that this improvement was driven by larger EEG response differences for global effects and more stable trial-to-trial neural states. In a cohort of 20 DOC patients, the proposed evaluation criteria achieved an individual diagnostic balanced accuracy of 68.8% (sensitivity = 50%; specificity = 87.5%), and a prognostic balanced accuracy of 60.5% (sensitivity = 50%; specificity = 71%). SIGNIFICANCE: These findings demonstrate that decoding sequential auditory expectations enables effective low.