# Cross-Subject Classification of Effectiveness in Performing Cognitive Tasks Using Resting-State EEG
## 4 Discussion  
This study tested the possibility of distinguishing volunteers with an advanced level of education in mathematics from individuals with a basic level of education in mathematics based on the frequency parameters of the resting-state EEG recorded before the start of cognitive tasks. Additionally, the volunteers were divided into groups of highly successful and moderately successful according to their task-solving performance. The Light Gradient Boosting Machine Learning algorithm was used for cross-subject classification based on the power spectral density of seven EEG frequency bands. The classification accuracy was about or below the chance level in recognition of classes depending on the education or success in task-solving. The subject-specific EEG was accurately classified (>70%) only for classes of individuals with advanced math education and participants with basic math education who showed the most successful performance in task-solving (a high rate of correct answers with quick decision time). It is known that EEG signals vary greatly not only from person to person but even from session to session [29]. The high inter and intrasubject variability of EEG impedes the effective transfer of features obtained by machine learning models from one person's data to another that is required for cross-subject EEG classification [22,30]. Several studies attempted to perform cross-subject individual classification where test and training EEG samples belonged to different individuals. A cross-subject classifier based on common special pattern recognition was developed to separate two mental states with an accuracy of up to 75.30%: the emotion of happiness and the imagination of movement [23]. In 2021, [22] the ability of artificial neural networks to produce cross-subject classification of fatigue with high performance, up to 91.63%, was reported. Unlike previous studies, our results firstly demonstrate the possibility to recognize individuals with specific education based  
Figure 4: The thirty mean Shapley Additive Explanations (SHAP) values for power spectral density (PSD) features, which had the most significant impact on the accuracy of resting-state EEG belonging to the most successful participants with advanced math skills (SAM) versus resting-state EEG belonging to the most successful participants with basic math skills (SBM): (**a**) top 30 highest SHAP values for frequency bands/electrodes; (**b**) electrode positions with the top 30 highest SHAP feature values. on their resting-state EEG oscillatory patterns. At the example of classes labeled depending on specific education and performance in cognitive task-solving, this current study showed the possibility to perform rather successful cross-subject classification and perspectives for further research in this direction. We did not find any differences between the AM and BM groups either by cluster permutation test or by a machine learning approach when we used the resting-state EEG data of all participants. Moreover, a classification model failed to distinguish resting-state EEG of individuals who showed worse performance in task-solving. However, light GBM showed good accuracy in labeling the data of the AM and BM group participants only with high/successful performance in task-solving (6 SAM and 6 SBM). The average accuracy and AUC were above chance for 4 folds out of 6 (Figure 2), which gave a mean ROC-AUC higher than 0.73. We may hypothesize that higher performance in task-solving resulted from more focused attention, mental concentration, and motivation that could be reflected in the brain rhythmic activity in the resting-state as well. Wherein patterns of this brain activity differed in individuals with advanced math education and without it sufficiently for the machine learning model to distinguish EEG of subjects from AM and BM groups. The obtained results are partially consistent with previous findings showing differences in the spectral power of the theta rhythm between AM and BM groups [11].