# Eye State Identification Based on Discrete Wavelet Transforms
## 1 Introduction
Furthermore, EEG eye state identification has been employed for the interaction with BCIs. For instance, [PERSON] et al. [26] present a home automation control system for a rapid ON/OFF switch appliance. This calculates a threshold employing alpha band to determine the user's eye state and control external devices. Due to the wide variety of areas where the EEG eye state detection can be applied, several methods have been presented to achieve higher classification accuracies. In this sense, [PERSON] and [PERSON] [27] tested 42 classification algorithms in terms of their performances to predict the eye state. For this purpose, a dataset containing the two possible ocular states was recorded using the 14 channels of the Emotiv EPOC headset. The reported results showed that standard classifiers such as naive Bayes, Artificial Neural Networks (ANNs) or Logistic Regression (LR) offered poor classification accuracies, while instance-based algorithms such as IB1 or KStar offered significantly higher results. The latter classifier achieved the best performance with a classification accuracy of 97.30%. However, it took at least 20 minutes to classify the state of new instances. Moreover, the dataset included the data of only one subject, so the authors cannot assure that the obtained results are generalizable. Several works have presented new classification methods based on this dataset. For example, [PERSON] et al. [17] proposed to extract channel standard deviations and averages as features for an Incremental Attribute Learning (IAL) algorithm and achieve an error rate of 27.45% for eye state classification. In a more recent study, [PERSON] et al. [28] propose to study the maximum and minimum values in the EEG signals in order to detect any eye state change. Once this change has been detected, the last two seconds of the signal are low-pass filtered below 8 Hz and passed through Multivariate Empirical Mode Decomposition (MEMD) for feature extraction. These features are fed into a classification algorithm to confirm the eye state change. For this purpose, they tested ANNs, LR and SVM. Their proposed algorithm using LR as a classifier detected the eye state with an accuracy of 88.2% in less than 2 s. [PERSON] et al. [29] proposed a new system based on eager learners (e.g., decision trees) in order to improve the classification time achieved by [PERSON] and [PERSON] [27]. For this purpose, three ensemble learners were evaluated: a rotational forest that implements random forests as its base classifiers, a rotational forest that implements J48 trees as its base classifiers and is boosted by adaptive boosting, and an ensemble of the rotational random forest model with the KStar classifier. The results achieved in the study showed that the approach using J48 trees and adaptive boosting offered accurate classification rates within the time constraints of real-time classification. Although the aforementioned papers show methods to detect eye states with high accuracy, they usually gather the brain activity using a large number of electrodes and voluminous EEG devices, which might be cumbersome and uncomfortable for real-life applications. In order to avoid these limitations, we present an EEG-based system that employs a reduced number of electrodes for capturing the brain signals. For this purpose, we extend our prototype presented in [16] to the case of two input channels in order to build a multi-dimensional feature set that improves the detection rates and reduces the response time of the system. We study and compare two algorithms with low computational complexity for eye state detection. For feature extraction, we employed the Discrete Wavelet Transform (DWT), which presents lower computational complexity than other widely known algorithms such as the Fast-Fourier Transform (FFT) [30]. For feature classification, we applied Linear Discriminant Analysis (LDA), a popular technique in BCI systems, which also presents low computational requirements [23; 31]. The paper is organized as follows.