receiving an electroencephalography (EEG) signal from a sensor electrode attached to a patient, the EEG signal comprising a plurality of channels;

segmenting the EEG signal into smaller epochs for each channel;

extracting features of the EEG signal in each epoch, the features comprising time domain features, frequency domain features, and entropy domain features;

determining a median of features among the plurality of channels for each epoch;

determining, by a classifier, a probabilistic estimate of a patient sedation, wherein the classifier is trained using training data comprising MOAA/S scores, the training data selected to exclude MOAA/S scores in a center range of the MOAA/S scores;

generating, using a determined correlation, a sedation level estimate, the sedation level estimate comprising a continuous sedation score; and

displaying an indication of the sedation level estimate.