## 3 Results
### Detection of Cognitive Activity  
We considered a person-independent scenario for the detection of cognitive activity. Specifically, using the physiological signals available in the MMD-MSD dataset, we aimed at the discrimination of task-specific mental workload. For that purpose, we used the elicited task-specific cognitive activity (Stroop Test #1 and Stroop Test #2) vs. the baseline active listening/answering while the volunteer (a) was instructed and filled in the entry questionnaires and (b) talked about their experiences during the Stroop tests with and without the stability disk. In this scenario, the two-class classification was based on feature vectors computed from the peripheral physiological signals (photoplethysmography (PPG) and galvanic skin response (GSR)), following the methodology outlined in the reference [38]. When compared to [38], we did not use the (i) mean absolute value of the peaks, (ii) skewness, or (iii) kurtosis of the peak's distribution, because these were found to be loosely correlated with the cognitive load detection task. However, an additional feature was added -- the ratio between the short- and long-term variability of PPG (i.e., the SD1-to-SD2 ratio). Therefore, we considered feature vectors consisting of 37 attributes, as outlined in Table 8.  
Following the procedure outlined in Section 2.7, we used ten-fold cross-validation in all experiments for cognitive load detection based on peripheral physiological data. Experimenting with the six classification methods, we noticed that the feature selection process of RapidMiner Studio tended to reject four of the features outlined in Table 8, due to their low correlation with the task labels. These were the (i) min NN interval, (ii) mean resistance, (iii) percentile 2.5, and (iv) percentile 10. Therefore, we excluded these, and all results reported here are for a feature vector consisting of the remaining 33 features. In addition to the raw feature vector, we also considered a z-normalized feature vector, where each attribute was adjusted to zero mean and unit standard deviation. In Table 9, we report comparative results in terms of average classification accuracy for the 100 participants in the MMD-MSD dataset. We compared the averaged classification accuracy for the generalized linear model with LBFGS solver; naive Bayes with Laplacian correction; decision tree C4.5 with leaf size one, accuracy optimization criterion, and max depth 15; support vector machine (SVM) with RBF kernel, with the regularization parameter C equal to 10 and gamma equal to 0.005; random forest with 20 trees and max depth 7; and a feed-forward neural network classifier with four layers (33-50-50-2), where the first three layers consist of neurons with ReLU activation functions, and the two output neurons have \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Signal** & \(\mathbf{\mathcal{N}}\) & **Description** & **Features** \\ \hline \multirow{8}{*}{PPG} & \multirow{4}{*}{\(1\div 12\)} & \multirow{4}{*}{Time-domain statistics on the signal} & Mean heart rate; mean RR; mean and max NN interval; pNN50; SDNN; RMSSD; standard deviation of the difference of successive NN intervals; SD1 (short-term variability); SD2 (long-term variability); SD1-to-SD2 ratio \\  & & & \\ \cline{2-4}  & & & Power in VLF (0–0.04) Hz, LF (0.04–0.15) Hz, and HF (0.15–0.4) Hz bands; normalized powers in the LF and HF bands; the power in the three bands in percent; HRV \\ \hline \multirow{8}{*}{GSR} & \multirow{2}{*}{\(22\div 27\)} & \multirow{2}{*}{Time-domain statistics on the SCL peaks} & Number of peaks; max and min amplitude of the peaks; mean conductance and standard deviation of the peaks; RMS \\  & & & \\ \cline{1-1} \cline{2-4}  & & & Power in band (0–2.4) Hz \\ \cline{1-1} \cline{2-4}  & & & Mean resistance; first quartile; second quartile; third quartile; interquartile range; percentile 2.5; percentile 10; percentile 90; percentile 97.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The complete set of PPG- and GSR-based features. softmax activation functions.