We use an electroencephalogram (EEG) to detect the arousal states of humans. The EEG patterns fluctuating between waking and sleep are described as several features extracted from the moving time windows. The significant feature, mean frequency (MF), is used for arousal states detection. The fluctuations of the EEG mean frequency are characterized as hidden Markov models (HMMs), which well estimate the next possible arousal state. In this HMM, single current-to-next state transition probability is considered along with global states transitions. Both local contextual effects (LCE) and global contextual effects (GCE) autoregressive HMMs are used to estimate and detect the transitions from waking to sleep. The validity of our proposed model is verified via a behavior measure-the correct rate of the subject's responses to auditory stimuli. In our study, the estimated values of mean frequency by GCE HMM show high correlation with the behavior measure. This high recognition rate makes arousal states detection practicable. Furthermore, the LCE HMMs are also applicable for artifacts rejection. In real-time arousal states detection, alarms are given whenever the subject's vigilant states turn into drowsy states. This system has many applications, for example, it can be used to maintain long term vehicle driver's arousal.
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