# Eye State Identification Based on Discrete Wavelet Transforms
## 5 Experimental Results
### Scheme 2: Two Features  
For this scheme, two extracted features were employed for the prediction of the eye state of the user: \(SD_{4}\) and the ratio \(R\). Windows with a duration of 10 s and a delay of \(D-d=2\,\)s were employed. From one realization of the cross-validation process, the extracted features of the training set are represented in Figure 7, where the decision boundary of LDA is also marked. The signals corresponding to three windows are shown in Figure 8 with their detail coefficients D3 and D4. Three situations are compared: OE without artifacts (Figure 8a-c), OE with blink artifact (Figure 8d-f) and CE state (Figure 8g-i). Figure 7 marks the features corresponding to these three windows. We see that they have been correctly classified, even the one with the blink artifact since the window size is larger than the artifact duration. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Wavelet**} & \multirow{2}{*}{**Filter Length**} & \multicolumn{2}{c}{**Closed**} & \multicolumn{2}{c}{**Open**} \\ \cline{3-6}  & & **O1 and O2 (\%)** & **O2 (\%)** & **O1 and O2 (\%)** & **O2 (\%)** \\ \hline db2 & 4 & 86.29 & 86.97 & 74.63 & 72.11 \\ db4 & 8 & 88.23 & 89.83 & 81.03 & 79.43 \\ db8 & 16 & 92.46 & **92.69** & 85.14 & 84.00 \\ coif1 & 6 & 86.97 & 87.89 & 76.34 & 75.54 \\ coif4 & 24 & 91.66 & 92.46 & **85.37** & **84.23** \\ haar & 2 & 86.74 & 89.03 & 73.14 & 71.09 \\ sym2 & 4 & 86.29 & 86.97 & 74.63 & 72.11 \\ sym4 & 8 & 90.17 & 91.31 & 81.49 & 80.00 \\ sym10 & 20 & **94.63** & 92.46 & 84.46 & 82.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average accuracy (in %) of all the subjects obtained for each wavelet type and ocular state for one and two sensor data using Scheme 1. Bold values indicate the highest value of each column. Filter length column represents the number of filter coefficients employed for the multi-resolution analysis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{2}{c}{**Closed**} & \multicolumn{2}{c}{**Open**} \\ \cline{2-5}  & **O1 and O2 (\%)** & **O2 (\%)** & **O1 and O2 (\%)** & **O2 (\%)** \\ \hline
1 & 100.00 & 100.00 & 94.40 & 91.20 \\
2 & 83.20 & 84.00 & 84.80 & 83.20 \\
3 & 100.00 & 100.00 & 96.00 & 96.00 \\
4 & 9  
Figure 6: Accuracy obtained for each subject as a function of the window size using Scheme 1. Figures (**a**–**g**) represent the accuracy for Subjects \(1\) to \(7\), respectively. Figure (**h**) shows the average accuracy of all the subjects. Figure 8: EEG signals captured from one of the participants from channel O2 and its wavelet decomposition for levels 3 and 4: **(a-c)** show the signal captured for OE without artifacts, its detail coefficients from D3 and D4, respectively; **(d–f)** show the signal captured for OE with one blink artifact, its detail coefficients from D3 and D4, respectively; **(g–i)** show the signal captured for OE, its detail coefficients from D3 and D4, respectively. Figure 7: Training features and LDA decision boundary for one training set of the cross-validation process. OE-Blink, OE-No blink and cE * represent the features obtained from the signals shown in Figure 8.  
Table 4 shows the mean accuracy of all the subjects obtained for each wavelet type and ocular state for one and two sensor data. All the wavelet types offer high performances for both eye states with an average accuracy above 91%, regardless of the number of channels employed. Similar results are obtained with one or two sensors, although the latter are slightly better. In addition, we can see that db8 offers the highest results for 3 of the 4 conditions, thus it could be the best choice for implementing the system. Table 5 shows the accuracy obtained for each subject, each eye sate and for one and two sensor data using db8 as mother wavelet, a window duration of 10 s and a delay of \(D-d=2\) s. Results from OE are higher than those achieved from CE. Results from one and two channels are similar, so the use of only one channel could be enough for a reliable performance of the system.