# A Three-Class Classification of Cognitive Workload Based on EEG Spectral Data
## 5 Discussion  
The main focus of the present research was put on estimation of cognitive workload level in accordance with subject-independent approach applying classical machine learning methods combined with feature selection techniques. Another issue addressed was multiclass classification of cognitive workload, i.e., distinguishing among more than two levels of cognitive workload. The results obtained show that the proper procedure of feature selection has high influence on the performance of the classification model, allowing to conduct subject-independent multiclass cognitive workload classification with high accuracy (91% in the case of the cross validation for kNN classifier). All the combinations of the mentioned classifiers and feature selection methods were tested, whereas only those feature selection methods that ensured the best values for specific classifiers, were presented, and the rest of the results were discarded from further analysis due to lower classification performance. Another point that can be highlighted is the procedure of feature selection. The results show that it allows to achieve sufficient performance of the classification model with a significant decrease of feature number: 52 of 95 features allowed to obtain the classification accuracy as high as 91% with the use of a kNN classifier. The comparison of the classification models applied shows that kNN and the ensemble of boosted decision trees allow to achieve the highest accuracy compared to the other models. The literature review shows that kNN methods were widely applied in cognitive workload classification in both two- and multiclass approach. The obtained accuracy varied in the range between 85% and 90% in case of subject dependent approach [36; 44; 47; 51; 53]. As for subject-independent approach, the results achieved with the use of kNN were in the range between 80% and 90%. The procedure of feature selection made it possible to achieve higher classification accuracy. The literature (papers presented in the Table 1) allows concluding that researches rarely conduct comparison of various methods of feature selection, limiting FS only to one method, or avoiding it at all. The results show that the choice of the classifier also affects the accuracy of distinguishing between various levels of cognitive workload. The task of classifying cognitive workload appears to be highly complicated due to high nonlinearity of the feature space classified. Nowadays, deep learning is progressively gaining popularity in many fields of data analysis, but classical models can still present interest due to lower computational cost of these models. The classical models can be trained faster with appliance of less powerful hardware [68]. In the present research, it was shown that the classical methods of machine learning (e.g., kNN, random forests) can provide good performance in the task of classifying cognitive workload with comparatively low computational cost. Another advantage of the classical classification methods is the fact that the results of classification are more interpretable. A deep neural network usually behaves as a \"black box\", in other words, it can approximate highly complex dependencies, but high number of model parameters cause bad interpretability of the results, whereas classical models, such as decision trees and kNN classifiers, give a better ability to understand the processes behind the analyzed phenomena. To complement the classification analysis, the statistical examination was also performed. Spearman analysis results indicated significant correlation between the cognitive workload level and EEG features of beta waves located especially in central and parietal areas of the brain. Results of ANOVA for repeated measures prove that the power of waves from the entire studied frequency range was significantly stronger during low and high cognitive workload, than it was observed in the recording in the relaxation condition.