# Eye State Identification Based on Discrete Wavelet Transforms
## 1 Introduction  
During recent decades, eye gaze analysis and eye state recognition have made up an active research field due to their direct implication in emerging areas such as clinical diagnosis or Human-Machine Interfaces (HMIs). The ocular state of the user and his/her gaze movements can reveal important features from its cognitive condition, which can be crucial for health care purposes but also for the analysis of daily life activities. Hence, it has been studied and applied in several domains such as driver drowsiness detection [1, 2, 3], robot control [4], infant sleep-waking state identification [5] or seizure detection [6], among others [7, 8]. Different techniques have been proposed for studying eye gaze and eye state, such as Video-oculography (VOG), Electro-oculography (EOG) and Electroencephalography (EEG). In VOG [9, 10], several cameras record videos or pictures of the user's eyes and, by applying image processing and artificial vision algorithms, provide an accurate analysis of the eye state of the user. In EOG [11, 12, 13, 14, 15], some electrodes are placed on the user's skin near to the eyes in order to capture the electrical signals produced by the ocular activity. On the other hand, in the EEG technique [16, 17], the electrical signals produced by the brain are measured using electrodes placed on the scalp of the user. The computational complexity associated with the algorithms employed in the image-based methods, such as VOG, is considerably higher than those used in EOG and EEG due to the costly process of analyzing and classifying multiple images [18]. The EOG method seems to be an interesting technique for building HMIs based on eye movements or blinking, but the placement of electrodes on the user's face might be uncomfortable and not usable in practical applications [19]. Thus, the EEG technique is an attractive solution for developing new interfaces that, based on the eye state of the user, can analyze and infer its cognitive state (relaxed, stressed, asleep, etc. ), which could be crucial information for the implementation of real applications. EEG is a popular technique for neuroimaging and brain signal acquisition widely used in the study of brain disorders [20] and in Brain-Computer Interface (BCI) systems [21]. EEG has several advantages such as its high portability and temporal resolution, its relatively low cost, and its ease of use [22; 23] when compared to other brain signal acquisition techniques such as Magnetoencephalography (MEG), Electrocorticography (ECoG) or functional Magnetic Resonance Imaging (fMRI). Particularly, EEG-based eye state detection has been applied in several domains, such as, for example, clinical diagnosis and health care. In this regard, [PERSON] et al. [6] propose a technique based on EEG time series and Power Spectral Density (PSD) features and on the use of a Recurrent Neural Network (RNN) for its classification. Their technique distinguished a relaxed and open eye state from an epileptic seizure with an accuracy of 100%. In another study on the same data set, [PERSON] et al. [24] propose the employment of Convolutional Neural Networks (CNNs) for the development of a Computer-Aided Diagnosis (CAD) system that automatically detects seizures. Their technique achieves an accuracy, specificity, and sensitivity of 88.67%, 90.00%, and 95.00%, respectively. Moreover, EEG-based eye state detection has been successfully applied for automatic driver drowwsiness detection. [PERSON] et al. [25] proposed to use Support Vector Machine (SVM) as classification algorithm to identify and differentiate EEG changes that occur between alert and drowsy states. Four main EEG rhythms (delta, theta, alpha and beta) were employed for extracting different frequency features, such as dominant frequency, frequency variability, center of gravity frequency and the average power of dominant peak. Their method reached a classification accuracy of 99.30% and was also able to predict the transition from alertness to drowwsiness with an accuracy over 90%.