# Cross-Subject Classification of Effectiveness in Performing Cognitive Tasks Using Resting-State EEG
## 3 Results
### Group Differences and Cross-Subject Group Classification  
The permutation cluster test did not show significant differences in the PSD of resting-state EEG between the HS and MS groups nor between the AM and BM groups. The BA of cross-subject classification of two different types of comparisons (M vs. BM and HS vs. MS) was almost random. Intrasubject classification BA for LAM and LBM was below chance: BA = 0.36 \(\pm\) 0.24, AUC = 0.28 \(\pm\) 0.31. Only the classification of SAM and SBM subjects was successful: BA = 0.7 \(\pm\) 0.23, AUC = 0.75 \(\pm\) 0.24. The results of LAM versus LBM and SAM versus SBM are depicted in Figure 2.  
Figure 3 illustrates SHAP values of feature contributions and their topography, and the ROC-AUC for SAM and SBM classification. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**Verbal**} & \multicolumn{2}{c}{**Arithmetic**} & \multicolumn{2}{c}{**Logical**} \\  & **CA** & **DT** & **CA** & **DT** & **CA** & **DT** \\ \hline AM & 78.19 \(\pm\) 11.16 & 11.59 \(\pm\) 3.80 & 78.61 \(\pm\) 10.51 & 18.83 \(\pm\) 3.79 & 77.22 \(\pm\) 17.40 & 15.75 \(\pm\) 4.45 \\ \hline BM & 76.19 \(\pm\) 13.28 & 12.14 \(\pm\) 4.50 & 65.71 \(\pm\) 17.57 & 22.71 \(\pm\) 4.16 & 69.64 \(\pm\) 21.01 & 18.21 \(\pm\) 3.53 \\ \hline HS & **49.77 \(\pm\) 5.33** & **9.58 \(\pm\) 3.40** & **49.15 \(\pm\) 4.58** & **17.04 \(\pm\) 2.85** & **49.00 \(\pm\) 9.30** & **12.80 \(\pm\) 2.06** \\ \hline MS & **42.77 \(\pm\) 7.46** & **13.67 \(\pm\) 3.30** & **36.85 \(\pm\) 9.28** & **22.35 \(\pm\) 2.38** & **38.77 \(\pm\) 11.83** & **18.23 \(\pm\) 2.28** \\ \hline SAM & 50.33 \(\pm\) 5.39 & 9.66 \(\pm\)4.09 & 50.33 \(\pm\) 5.65 & 15.49 \(\pm\) 2.43 & 49.00 \(\pm\) 12.95 & 11.42 \(\pm\) 1.61 \\ \hline SBM & 50.50 \(\pm\) 5.09 & 8.92 \(\pm\) 2.85 & 48.17 \(\pm\) 3.92 & 18.44 \(\pm\) 2.86 & 49.83 \(\pm\) 5.85 & 14.15 \(\pm\) 1.74 \\ \hline LAM & 43.50 \(\pm\) 6.44 & 13.11 \(\pm\) 2.69 & 44.00 \(\pm\) 5.62 & 20.43 \(\pm\) 2.32 & 43.67 \(\pm\) 7.42 & 17.01 \(\pm\) 3.76 \\ \hline LBM & 38.67 \(\pm\) 5.79 & 15.34 \(\pm\) 2.87 & 32.50 \(\pm\) 10.44 & 23.50 \(\pm\) 2.43 & 32.50 \(\pm\) 13.69 & 18.81 \(\pm\) 1.17 \\ \hline \end{tabular}
\end{table} 
Table 3: Averaged results (mean \(\pm\) standard deviation) of behavioral performance (CA—correct answers in % and DT—decision time) for the studied groups. Figure 2: Results of the cross-subject classification: the left panel shows the least successful individuals with an advanced level of education in mathematics versus the least successful individuals with a basic level of education in mathematics; the right panel shows the most successful individuals with an advanced level of education in mathematics versus the most successful individuals with a basic level of mathematics. The \(Y\)-axis represents values of balanced accuracy of classification (blue bars) and values of the area under the curve (pink bars). The black line is the chance level. The SHAP approach yielded information about the contributions of all features to the classification. PSD of the high theta, alpha, and beta frequency bands at specific electrodes had a significant impact on the correct recognition of the classes (SAM or SBM). Figure 4 illustrates the 30 most important features (out of 600 in total) for the classification according to SHAP values and localizations of sensors.