# Mental Workload Classification Method Based on EEG Independent Component Features
## 3 Results of Classification Method Comparison
### Performance Evaluation of Different Methods  
The statistical hypothesis test provides an important basis for the learning performance comparison of methods in this paper. Based on the results of the hypothesis test, we can infer which one has better  
Figure 8: Classification accuracy comparison of Method 3 and Method 4.  
Figure 7: Implementation of Method 3 (**a**) and Method 4 (**b**).  
performance based on the same test set. The \"hypothesis\" in the hypothesis test is generally set to compare the performance of the two learning methods with the same significance.  
In order to evaluate the performance of four different methods, Method 1 to Method 4, this paper chose the cross-validation paired t-tests for verification. The cross-validation requires independent sampling with a test error rate of generalized error rate. But due to the limited number of segments, the training sets of different rounds will overlap to a certain extent. In order to overcome this problem, this paper used five iterations of 2-fold cross-validation [41] (pp. 24-44).  
We performed five replications of 2-fold cross-validation. In order to prevent the data division in five cross-validations from being repeated, the data was randomly shuffled before each 2-fold cross-validation. In each replication, the first fold cross-validation test error rates, \(e_{11}\) and \(e_{12}\), and second fold cross-validation test error rates, \(e_{21}\) and \(e_{22}\), were obtained. Their differences were calculated using Equation (6):  
\[\Delta_{1}=|e_{11}-e_{21}|\] \[\Delta  
From Table 2, we can observe that:  
(1) The range of \(\tau_{1}\) is between 4.103 and 9.678. Each value is greater than the critical value, 2.5706, so Hypothesis 1 can be rejected, that is, the performance of Method 1 and Method 2 is significantly different. At the same time, it can be known from the classification accuracy that the error rate of Method 1 is lower than that of Method 2, so the Method 1 performs well. \(\tau_{3}\), \(\tau_{4}\) and \(\tau_{6}\) can be analyzed in the same way.  
(2) The range of \(\tau_{2}\) is between 0.763 and 2.279. Each value is lower than the critical value, 2.5706, so Hypothesis 2 can be accepted, that is, the performance of Method 1 and Method 3 is not significantly different. At the same time, it can be known from the classification accuracy that the error rate of Method 3 is similar to Method 4, so there is no significant difference in the performance comparison of Method 1 and Method 3. \(\tau_{5}\) can be analyzed in the same way.  
It can be concluded from the above analysis that the method proposed in this paper has better classification performance than other methods, regardless of whether the artifact is removed or not.  
However, the accuracies of Method 2 and Method 4 are relatively low. They realize the classification based on features of EEG signals. EEG signals are more difficult to analyze than EEG independent components because EEG signals are mixed signals. Just liking mixed speech signals, it is generally known that it is easy to identify the content of the speech signals for a single sound source. But for mixed sound sources, multiple speech signals are aliased, which makes the semantics ambiguous. So, the accuracy of Method 2 and Method 4 is relatively low.