Fig. 1.

Main EEG measures obtained in patients with disorders of consciousness: rest-state EEG, sleep EEG, event-related potentials (ERP), brain-computer interface (BCI) and transcranial magnetic stimulation–electroencephalography (TMS-EEG). These measures provide various EEG characteristics based on different information extraction algorithms. The right panel summarizes the main EEG characteristics with respect to their potential values in classifying minimally conscious state (MCS) and vegetative state/unresponsive wakefulness syndrome (VS/UWS) (diagnostic value), outcomes prediction (prognostic value) and treatment monitoring (response to treatment). The colors identify EEG measures belonging to the main EEG techniques as indicated by the same colors in the left panel. ± in the ‘diagnostic value’ column indicate that EEG measures are larger (+)/smaller (–) in MCS than in VS/UWS. ± in the ‘prognostic value’ column indicate that larger EEG measures correspond to better (+)/worse (–) outcome. Finally, ± in the ‘response to treatment’ column means that EEG measures increased/decreased during treatment. KCC Kolmogorov–Chaitin complexity, wSMI weighted symbolic mutual information, dwPLI debiased weighted phase lag index, STEn symbolic transfer entropy, LPC late positive components, VMI visuomotor integration, VEP visual evoked potentials, MMN mismatch negativity, TEP TMS evoked potentials, GMFP global mean field power, PCI perturbational complexity index