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In an overnight driving simulation study three commercially available devices of fatigue monitoring technologies (FMT) were applied to test their accuracy. 16 volunteers performed driving tasks during eight sessions (40 min each) separated by 15 minutes breaks. The main output variable of FMT devices, which is the percentage of eye closure (PERCLOS), the driving performance (standard deviation of lateral position in lane, SDL), the electroencephalogram (EEG) and electrooculogram (EOG) were recorded during driving. In addition, the subjective self-rated Karolinska sleepiness scale (KSS) was assessed every 2 min. As expected, Pearson product-moment correlation coefficient (PMCC) yielded significant linear dependence between KSS and PERCLOS as well as between SDL and PERCLOS. However, if PMCC was estimated within smaller data segments (3 min) as well as without averaging across subjects then strongly decreased correlation coefficients resulted. To further validate PERCLOS at higher temporal resolution its ability to discriminate between mild and strong fatigue was investigated and compared to the results of the same analysis for EEG/EOG. Spectral-domain features of both types of signals were classified using Support-Vector Machines (SVM). Results suggest that EEG/EOG indicate driver fatigue much better than PERCLOS. Therefore, current FMT devices perform acceptably if temporal resolution is low (> 20 min). But, even under laboratory conditions large errors have to be expected if fatigue is estimated on an individual level and with high temporal resolution.