Extended Data Fig. 7: Diagnosis and outcome prediction using SVM.

a, The confusion matrix of diagnosed consciousness classification generated by the cross-validated SVM. The feature combinations we used were [∆Cρ + Duration_L-R+ Occurrence_A-P + ITPC_1Hz + ITPC_2Hz + ITPC_4Hz] for Sentence task. b, The performance of outcome prediction on training data using SVM classifier with the best feature combinations. Left: Outcome prediction accuracies by EEG on 38 EEG recordings (15 outcome-positive patients). Right: Comparison of individual predictions and actual outcomes. The patients with UWS are shown to the left of dashed line, and the patients with MCS are shown to the right. The dots above the threshold (gray line, prediction score = 0.3) represent the patients with predicted positive outcomes, while the others represent those with predicted negative outcomes. The actual outcome-negative patients are marked by orange dots, and the actual outcome-positive patients are marked by green diamonds. Solid green diamonds represent the outcome in patients that regained wakefulness. The feature combinations we used were: [∆Probability + Duration_L-R + Transition_A-P] for Word condition, [∆Probability + Occurrence_A-P + Duration_L-R + Transition_L-R + ITPC_4Hz] for Phrase condition, [Occurrence_A-P + Duration_L-R + Transition_L-R + ITPC_1Hz + ITPC_2Hz] for Sentence condition.