A system for monitoring a patient experiencing an administration of at least one drug having anesthetic properties, the system comprising: a plurality of sensors configured to acquire at least EEG data from the patient; a user interface configured to receive an indication of at least one of a characteristic of the patient and the at least one drug having anesthetic properties; at least one processor characterized in that the at least one processor is configured to: receive the EEG data from the plurality of sensors and the indication from the user interface; assemble the EEG data from the plurality of sensors into sets of EEG data forming time-series of EEG data; analyze the sets of EEG data using thresholding to generate binary time series from the sets of EEG data and using a burst suppression probability algorithm implementing a state-space model to generate a time-series of burst suppression probabilities from the binary time series, wherein burst suppression probabilities are related to the patient brain's instantaneous likelihood of being in a suppressed state; determine, using the time-series of burst suppression probabilities, signature profiles consistent with the administration of at least one drug having anesthetic properties; identify, using the signature profiles, at least one of a current state and a predicted future state of the patient induced by the at least one drug, based on the indication; and generate a report indicating at least one of the current state and the predicted future state of the patient induced by the drug. a plurality of sensors configured to acquire at least EEG data from the patient; a user interface configured to receive an indication of at least one of a characteristic of the patient and the at least one drug having anesthetic properties; at least one processor characterized in that the at least one processor is configured to: receive the EEG data from the plurality of sensors and the indication from the user interface; assemble the EEG data from the plurality of sensors into sets of EEG data forming time-series of EEG data; analyze the sets of EEG data using thresholding to generate binary time series from the sets of EEG data and using a burst suppression probability algorithm implementing a state-space model to generate a time-series of burst suppression probabilities from the binary time series, wherein burst suppression probabilities are related to the patient brain's instantaneous likelihood of being in a suppressed state; determine, using the time-series of burst suppression probabilities, signature profiles consistent with the administration of at least one drug having anesthetic properties; identify, using the signature profiles, at least one of a current state and a predicted future state of the patient induced by the at least one drug, based on the indication; and generate a report indicating at least one of the current state and the predicted future state of the patient induced by the drug. receive the EEG data from the plurality of sensors and the indication from the user interface; assemble the EEG data from the plurality of sensors into sets of EEG data forming time-series of EEG data; analyze the sets of EEG data using thresholding to generate binary time series from the sets of EEG data and using a burst suppression probability algorithm implementing a state-space model to generate a time-series of burst suppression probabilities from the binary time series, wherein burst suppression probabilities are related to the patient brain's instantaneous likelihood of being in a suppressed state; determine, using the time-series of burst suppression probabilities, signature profiles consistent with the administration of at least one drug having anesthetic properties; identify, using the signature profiles, at least one of a current state and a predicted future state of the patient induced by the at least one drug, based on the indication; and generate a report indicating at least one of the current state and the predicted future state of the patient induced by the drug.