# Eye State Identification Based on Discrete Wavelet Transforms
## 6 Discussion  
Several solutions have been proposed during recent decades for the detection of the eye state through EEG activity [17; 27; 29]. However, these solutions usually capture the brain signals using large and voluminous devices, which are cumbersome and uncomfortable for the final user. The main goal of the presented study is to develop a new system for eye state identification based on an open EEG device that gathers the brain activity using a reduced number of electrodes. For this purpose, the DWT and the LDA were applied for feature extraction and feature classification, respectively. Furthermore, different feature schemes are compared in order to determine which of them offers the best classification accuracy and response time. From Tables 2-5, we can see that the scheme, which considers two features (\(SD_{4}\) and the ratio \(R\)) offers higher results than those achieved by the scheme composed of a single feature for all the mother wavelets (Tables 2 and 4) and six of the seven subjects (Tables 3 and 5). This difference becomes more apparent for the oE case, especially when small window sizes are employed (see Figures 6 and 9). Moreover, considering that for the real implementation of the system an average accuracy greater than 80% is required for both ocular states, we can see from Figures 6h and 9h that Scheme 2 achieves it at 2 s, while Scheme 1 needs 6 s.  
Several shapes for wavelet functions have been proposed for the analysis of EEG signals, such as Haar, Daubechies (db2, db4 and db8), Coiflets (coif1, coif4) or Symlets (sym2, sym4, sym10). However, depending on the application or analysis where they are involved, a particular wavelet family will result in a more efficient performance than the others [33; 34; 35; 59]. Therefore, the selection of an appropriate mother wavelet is crucial for the correct performance of the system. From Tables 2 and 4, we can see the average results obtained for each wavelet type for both ocular states. For Scheme 1 with a single feature, there are remarkable differences between each one of the wavelets. Furthermore, it can be observed that the results for CE are significantly higher than those obtained for OE. Conversely, for Scheme 2, the results obtained by the different wavelets are very similar and there is no big differences between OE and CE. Therefore, this second approach should be selected for the implementation of the system in a real scenario since it offers more robust results. The response time of the system is also a key aspect when developing real-time and online applications. Consequently, we tested our system for small window sizes with short response times. Figures 6 and 9 show the results for each subject and eye state using coif4 with a single feature and db8 with the two features, respectively. As previously mentioned, Scheme 2 offers higher accuracy and more robust results than Scheme 1, especially for the OE case and small window sizes. Moreover, similar results are achieved for one and two-channel data in the case of Scheme 1. However, for Scheme 2, the results obtained by the two-channel data are higher for some subjects. This difference is more apparent for small size windows (see Figure 9a,c,e). Taking into account the filter lengths shown in Table 2, the number of operations needed to compute the db8 in Scheme 2 is considerably lower that the needed to compute the coif4 used in Scheme 1. We can conclude that Scheme 2, composed by the two features, is the most suitable option for implementing the system since it offers the best performance in terms of accuracy and response time. There is no significant difference between the use of one or two sensors for large window sizes; however, we consider that the use of both channels could be more suitable for the system since in some subjects it did show an improvement for small window sizes.