# Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography
## 4 Method
### Feature Extraction  
After pre-processing of the raw EOG signal to remove the IFR and baseline drift, as the input features for the model, the eigenvalues related to eye fatigue are extracted. Eye  
Figure 7: Comparison of raw EOG signal and processed EOG signal.  
rotation, according to the different rotation angles and amplitude, will produce the EOG signal with different directions and amplitude [15]. There are more than 20 features of EOG signal: Proportion of slow eye movements, the average amplitude of slow eye movements, the variance of slow eye movements, proportion of fast eye movements, peak speed of fast eye movements, average amplitude of fast eye movements, the variance of fast eye movements, blink duration, eye closure duration, eye-opening duration, eye-opening delay, eye-opening delay to blink duration ratio, blink interval, the average amplitude of blink, the peak speed of eye-opening, the average speed of eye closure, eye-opening average blink amplitude, peak eye closing velocity, peak eye-opening velocity, average eye closing velocity, average eye-opening velocity, horizontal eye low frequency to high-frequency energy ratio, vertical eye low frequency to high-frequency energy ratio, etc. However, not all of these features mentioned above correlate closely with eye fatigue, blindly extracting all these features as a basis for eye fatigue analysis could lead to the dimensional disaster in the model. To avoid this problem, this paper designs several different arrangement combinations of these characteristic values and carries on the verification experiment. In this verification experiment, the pairing \(t\)-test method is used to rank the importance of the two-two characteristics in descending order. As the result, 5 features of 5 EOG signals, which are greatest correlated with eye fatigue, were selected. The 5 features contain slow eye movement features, fast eye movement features; energy peak frequency features, number and ratio of eye movement peaks features, and alignment entropy features. The following two illustrations (Figures 8 and 9) showed the extracted features and the rank of all features and the five selected features in the form of a directional diagram.  
Figure 8: (a) The extracted features as a directed graphical. (b) Correlation ranking of all features.  
Figure 9: _Cont_.  
As the Figure 8b shows, the ranking of correlation between all EOG features and eye fatigue is as follows: XPowerBand5 (Energy spectrum value), XSpectPos1 (Spectrum peak value), pe (Arrange entropy values), ratio_fati_high_40 (Percentage of right eye movement amplitude), ratio_fati_40 (Percentage of eye movement), high_integration_FFT (Spectrum integration), peak_fEOG (Number of peak right eye movement points), sum_movepeak (Total number of peak points), ratio_fati_low_40 (Proportion of left eye movement_1), ratio_fati_low_20 (Proportion of left eye movement_2).  
Figure 9b shows the correlation ranking of the 5 selected features. In the next several sub-chapters, the details and extraction methods for these five features would be described in detail below.