# Mental Workload Classification Method Based on EEG Independent Component Features
## 3 Results of Classification Method Comparison  
### Comparison of Mental Workload Classification Using EEG and its ICs Features  
This section will compare the classification accuracy by using the energy features of EEG ICs and just EEG. The former presented in this paper is named as Method 1, and the latter is named as Method 2. The implementation of the two methods is shown in Figure 5.  
Method 1: It is proposed in this paper and realizes the mental workload classification by using the energy features of EEG ICs. This method uses the energy features of ICA to separate the EEG signals and obtains its 30 ICs. The four energy features are then extracted from the EEG ICs. The calculation method is shown in Equations (5) and (6). There is a total of 900 segments for three mental workloads, and 120-dimensional energy features for each segment.  
Method 2: It realizes a mental workload classification by only using the energy features of EEG signals. This method directly extracts the energy features of the filtered EEG signals without using ICA to obtain the EEG ICs. The four energy features are extracted directly from each EEG segment.  
The classification accuracy of two methods is compared and shown in Figure 6.  
For the same data signals, the classification accuracy of the proposed method, Method 1, is in the range of 61% to 96%, and its average classification accuracy is 79.8%. The classification accuracy of Method 2 is between 54% and 64%, and its average classification accuracy is 55.9%. Compared with Method 2, the classification accuracy of Method 1 is higher by 13% to 37%, and the average classification accuracy is higher by 23.9%.  
Figure 4: Mental workload classification method based on features of EEC independent components (ICs).  
Comparison of Mental Workload Classification Using EEG and its ICs Features without Ocular artifacts(OAs)  
This section introduces the mental workload classification based on EEG and its ICs without OAs. In order to remove the OAs from the raw EEG signals and EEG ICs, Method 3 and Method 4 were exclusively built, as shown in Figure 7.  
In Method 3, as shown in Figure 7a, the OAs are removed from ICs of EEG using the ADJUST algorithm after the ICA process [40]. The ADJUST is a completely automatic algorithm that can identify artifact-related independent components by combining the artifact-specific spatial and temporal features. The ICs without OAs are directly extracted energy features, and then they are the input vectors for the SVM classifier.  
In Method 4, as shown in Figure 7b, the OAs are removed from ICs of EEG using the ADJUST algorithm after the ICA process. The ICs without artifacts are reconstructed to EEG without OAs using  
Figure 5: Implementation of Method 1 (**a**) and Method 2 (**b**).  
Figure 6: Classification accuracy comparison of Method 1 and Method 2.  
the independent component reverse reconstruction processing. The energy features are extracted from EEG signals without OAs, and then they are the input vectors for the SVM classifier.  
The accuracy comparison results of the above two methods are shown in Figure 8.  
Method 3 and Method 4 both use the energy features without OAs to classify the mental workloads. The difference between the two methods is that Method 3 uses the energy features of the EEG ICs, and Method 4 uses the energy features of the EEG. From Figure 8, we can observe that the classification accuracy of Method 3 is between 60% and 97%, and its average classification accuracy is 76.9%. The classification accuracy of Method 4 is between 50% and 64%, and its average classification accuracy is 54.7%. The classification accuracy of Method 3 is much higher than that of Method 4. The difference of their average accuracies is 22.2%.