# A Three-Class Classification of Cognitive Workload Based on EEG Spectral Data
## 4 Results  
### Statistical Analysis  
Statistical analysis was performed using the Statistica 13 (Dell Inc.), Matlab and R software. All tests were performed with the statistical significance level of 5%. Statistical analysis was conducted for features calculated as natural logarithm of separate frequency component power. Analyzed data subjected to statistical analysis had 285 features (19 electrodes, 5 frequencies, 3 cognitive workload levels). The sample size is 11. Statistical analysis was based on ANOVA for repeated measures. What is more, the correlation analysis was additionally performed to supplement the results. The values of Spearman correlation coefficients were computed to determine the relationship between features and the level of cognitive workload. Three levels of cognitive workload were considered: Cognitive workload level of 1 indicates break, 2-low cognitive workload level, and 3-high cognitive workload level. The results of this correlation analysis including p-value are presented in the Table 2. Presented results cover statistically significant correlation obtained at the level alpha 0.05. The correlation analysis confirmed the strongest correlation between the cognitive workload level and EEG features in beta waves covered especially in central and parietal areas of the brain. Laterality analysis revealed higher results for the electrodes on the left in the delta, theta, alpha, and beta2, but in the beta1 the trend is the opposite (F (4, 40) = 336.26, \(p<0.001\) obtained for the interaction of the electrode location with individual frequency ranges). ANOVA for repeated measures was applied in order to perform the main statistical analysis. The analysis was conducted on the \(11\times 285\) dataset, where 11 is a sample size (number of participants) and 285 is a number of features obtained after electrodes aggregation to the main brain areas. Results of ANOVA for repeated measures were calculated for three effects defined as: COGNITIVE (three levels of cognitive workload), LOCATIONS (nineteen electrodes), and FREQUENCY (five EEG waves). As a result of this analysis, a significant main effect of COGNITIVE F (2, 20) = 26.795 was obtained; \(p<0.001\); \(\eta_{p}^{2}=0.73\). After the application of Tukey HSD post hoc tests, it was observed that the power of waves from the entire studied frequency range was significantly stronger (\(p<0.001\)) during low (M = \(-0.61\); SE = 0.11) and high cognitive workload (M = \(-0.64\); SE = 0.11), than it was observed in the recording in the relaxation condition (M = \(-0.83\); SE = 0.11). Simultaneously, there are no significant  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Correlation Coefficient** & \(p\)**-Value** \\ \hline Cz beta1 & 0.35 & \textless{}0.001 \\ \hline Cz beta2 & 0.32 & \textless{}0.001 \\ \hline Fz beta1 & 0.27 & \textless{}0.001 \\ \hline C4 beta2 & 0.26 & \textless{}0.001 \\ \hline P4 beta2 &  
differences in power between low and high cognitive load (\(p=0.995\)). This relationship between cognitive workload and wave power over the entire frequency range is presented in the Figure 4, where it might be observed that the natural logarithm of wave power during cognitive workload is on around \(-0.61\) ln(mV\({}^{2}\)). Statistical analysis revealed also significant differences between all effects (COGNITIVExLOCATIONSxFREQUENCY). F(144, 1440) = 1.763; \(p<0.001\); \(\eta_{p}^{2}=0.15\). However, for this result post hoc analysis did not confirm any strong relations. Significant differences were also found in LOCATIONS (F (18, 180) = 13.867; \(p<0.001\)) and FREQUENCY (F (4, 40) = 845.21; \(p<0.001\)). What is more, results reveal significant interaction with the COGNITIVExLOCATION effect (F (36, 360) = 1.907; \(p<0.001\)) confirmed in post hoc analysis. Due to the fact, that inter-trial-intervals (breaks) represent a different brain state than low/high workload states, the correlation analysis was performed also for only two levels of cognitive workload: Low and high.