Alertness Assessment using Data Fusion and Discrimination Ability of LVQ-Networks Udo Trutschel 2 , David Sommer 1 , Acacia Aguirre2 2 Todd Dawson 2 , Bill Sirois 2 1 University of Applied Sciences Schmalkalden, 98574 Schmalkalden, Germany 2 Circadian Technologies, Inc., 2 Main Street, Suite 310 Stoneham, MA 02180 eMail: utrutschel@circadian.com Abstract. To track the alertness changes of 14 subjects during a night driving simulation study traditional alertness measures such Visual Ana- log Sleepiness Scale, Alpha Attenuation Test (AAT), and number of Mi- crosleep events per driving session were used. The aim of the paper is to assess these traditional alertness measures regarding their mutual cor- relations, revise one of them (AAT) and introduce new more general methods to capture changes in human alertness without too many con- straints attached. The applied methods are utilizing data fusion methods and data discrimination capabilities via Learning Vector Quantification networks. The advantage of using more general data analysis methods which allows one to assess the validity of proposed alertness measures and opens possibilities to get a more comprehensive knowledge of obtained results. 1 Introduction Recent technical developments have produced a 24-hour, advanced society that continues to grow on a global scale. Consequently, the basic human circadian rhythm (”working during the day and sleeping at night”) is under constant siege. Because of the long working hours that eat up people’s sleeping time, a general deterioration of people’s daytime alertness and an increase in driver drowsiness is seen. Especially, accidents caused by drowsy drivers have a high fatality rate and high costs. To prevent these accidents a reliable tool to accurately measure human alertness levels is needed. The first attempts to quantify human alertness were subjective reports that consisted of documenting the individual’s self-assessment. The main measures include the Stanford Sleepiness Scale (SSS), the Visual-Analog Scale (VAS), and the Epworth Sleepiness Scale (ESS). More objective measures of human alertness can be derived from electroencephalogram (EEG) and electrooculogram (EOG) data. For example, the Multiple Sleep Latency Test (MSLT) measures the time to fall asleep while lying in a quiet, dark bedroom on repeated opportunities at 2 hours intervals throughout the day using EEG for sleep onset determina- tion. The Maintenance of Wakefulness Test (MWT) requires that subjects sit in chairs in a darkened room and remain awake for 40 minutes. After applying different mathematical and statistical techniques, EEG-frequency bands (delta,