A drowsiness detection system using both brain and visual activity is presented in this paper. The brain activity is monitored using a single electroencephalographic (EEG) channel. An EEG-based drowsiness detector using diagnostic techniques and fuzzy logic is proposed. Visual activity is monitored through blinking detection and characterization. Blinking features are extracted from an electrooculographic (EOG) channel. Features are merged using fuzzy logic to create an EOG-based drowsiness detector. The features used by the EOG-based detector are voluntary restricted to the features that can be automatically extracted from a video analysis of the same accuracy. Both detection systems are then merged using cascading decision rules according to a medical scale of drowsiness evaluation. Merging brain and visual information makes it possible to detect three levels of drowsiness: “awake,” “drowsy,” and “very drowsy.” One major advantage of the system is that it does not have to be tuned for each driver. The system was tested on driving data from 20 different drivers and reached 80.6% correct classifications on three drowsiness levels. The results show that EEG and EOG detectors are redundant: EEG-based detections are used to confirm EOG-based detection and thus enable the false alarm rate to be reduced to 5% while the true positive rate is not decreased, compared with a single EOG-based detector.