## 6 Calculation of Pulse Wave Features  
Pulse wave features were extracted from images of faces taken from videos by setting the region of interest (ROI), estimating pulse waves by hemoglobin and shade separation and extracting pulse wave features in the time and frequency domains. First, a hemoglobin video image was obtained by applying dye-component separation to a facial video image captured using an RGB camera. Next, the participant's cheeks and nose were defined as the ROI, and the time series of the hemoglobin concentration components in this ROI were calculated as a pulse wave. The nose and cheeks were chosen because it is relatively easy to observe pulse wave changes in this facial region [33]. Hemoglobin values were averaged across pixels within a relatively large ROI for noise reduction. Given that raw pulse wave signals contain considerable noise, trend removal [34] and bandpass filtering were applied as preprocessing for feature calculation. To improve the accuracy of peak detection, a bandwidth was set, and the peaks were detected. The bandwidth settings were obtained from [PERSON] et al. [28]. Figure 1c shows an example of a signal preprocessed by trend removal and bandpass filtering, based on the time variation in the average pixel value in the ROI of a hemoglobin component image. The peak-to-peak interval in the pulse wave can be considered an approximation to the RR interval in an ECG. Heart rate variability, which is the variation in the time intervals between successive R waves, is known to reflect the balance between sympathetic and parasympathetic nervous system activation. Therefore, a nonlinear analysis of the peak-to-peak intervals in the time and frequency domains could make it possible to assess the functioning of the autonomic nervous system. Seven time domain features were obtained from estimated pulse wave signals. These included mean RR interval, standard deviation of RR intervals, mean heart rate, standard deviation of heart rate, Root Mean Square of Successive Differences (RMSSD), the number of successive RR intervals that differ by more than 50 ms (NN50) and the percentage of successive RR intervals that differ by more than 50 ms (pNN50) [35]. While the standard deviation of the RR interval reflects the degree of overall heart rate variability, the RMSSD reflects the degree of short-term heart rate variability, particularly variability due to parasympathetic activity. NN50 and pNN50 are parasympathetic indicators [35]. \begin{table}
\begin{tabular}{c c c} \hline Number of participants & & 25 \\ Number of classes & 2 (High- vs. low-motivation state) & \\ Number of video data in each class &
\begin{tabular}{c} High-motivation state \\ Low-motivation state \\ \end{tabular} & 16 \\ Maximum number of frames & 12,945 frames & \\ Minimum number of frames & 10,564 frames & \\ Average number of frames & 11,454 frames & \\ Standard deviation of the number of frames & 556 frames & \\ \hline \end{tabular}
\end{table} 
Table 1: Description of the data set submitted for final analysis. Time series of peak-to-peak intervals were Fourier-transformed, and ten frequency domain features, summarized in Table 2, were calculated to quantify parasympathetic and sympathetic activations. The converted low-frequency component (LF: 0.04-0.15 Hz) represents Mayer waves acting on both sympathetic and parasympathetic nerves, while the high-frequency component (HF: 0.15-0.4 Hz) shows a strong correlation with respiratory sinus arrhythmia, which is affected by respiration and parasympathetic action [34]. Therefore, LF/HF, LF/(LF + HF) and HF/(LF + HF) ratios can be used for sympathetic activity evaluation. Respiratory rate was estimated based on the frequency with the highest power spectrum density within HF range. In total, 17 different features were extracted from videos recorded of each participant. Some of these features had a high correlation (correlation coefficient \(\geq\) 0.4). In the logistic regression analysis, collinearity among the features makes the analysis unstable.