# A Photoplethysmogram Dataset for Emotional Analysis
## 2 Materials and Methods  
### Dataset Acquisition  
Initially, PPG signals were recorded by 18 participants (13 men and 5 women), with ages ranging from 23 to 31. None of them had a history of heart disease, and they did not consume alcoholic products within 12 h before the start of the data recording. During data acquisition, each participant watched short video clips that were expected to stimulate emotional responses. Table 1 provides detailed information on the participants. The target emotions of this study were joy, sadness, anger, and relaxed, such that the emotions represent each quadrant of the emotional space of arousal and valence (see Figure 1). This emotional space has been widely utilized in emotional recognition studies [22; 28; 29; 30; 32; 33]. The data were recorded from a pulse sensor [26] connected to Arduino with the sampling rate set to 100 Hz, while participants watched a video clip. The video clips were played on a monitor while the participants were seated in front of the monitor. The sensor was attached at a fingertip with Velcro tape. Fingers to record signals were different for participants because PPG signals were not measured precisely at certain fingers. A simple python GUI (graphical user interface) software was developed to record signals from Arduino. The software was run on a personal computer which was connected to Arduino via a serial communication. The order of the emotional stimuli was relaxed, joy, sadness, and anger. Rests of 3 min were given to the participants after watching video clips of joy and sadness. The order of the stimuli was fixed in this experiment because it was reported that the order of the stimuli was not significantly related to the ratings of arousal and valence even when little breaks were given between the stimuli [30]. The participants rated subjective degrees of emotional stimuli after watching a video clip. The sampling rate of 100 Hz is relatively lower than the other dataset, but it still covers the most frequency bands of interests. Features have been extracted in 0.67-50 Hz band for \begin{table}
\begin{tabular}{c c c} \hline \hline
**Participant \#** & **Sex** & **Age (Years)** \\ \hline
1 & Male & 26 \\
2 & Female & 30 \\
3 & Male & 26 \\
4 & Male & 26 \\
5 & Male & 25 \\
6 & Male & 25 \\
7 & Male & 23 \\
8 & Female & 23 \\
9 & Male & 23 \\
10 & Male & 28 \\
11 & Female & 20 \\
12 & Male & 26 \\
13 & Female & 24 \\
14 & Female & 27 \\
15 & Male & 25 \\
16 & Male & 31 \\
17 & Male & 23 \\
18 & Male & 23 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Participant information. Figure 1: Arousal and valence model. ECG in general [12], and recent studies utilized the frequency lower than 0.4 Hz for emotion recognition with PPG [29; 33; 34]. Figure 2 shows a sample PPG signal of the obtained data. Two video clips were chosen for each emotional state, as listed in Table 2. Each participant first watched a video, and a second video was played if the participant felt that their emotion was not evoked. This decision was dependent on the participants' subjective opinions, and the participants did not describe the degree of the emotional stimulus in this case. Rests were provided to the participants between each video clip, and the duration of the resting period was decided by the participants. Monetary rewards were provided to the participants. The verbal language in the video clips was Korean because all participants were native Koreans. Video clips were selected in YouTube, whose playtime was shorter than 5 min but impressive enough to invoke the emotions of the participants. Most of the video clips were viewed more than one million times. The video clips for joy were selected among a comedy video contest in Korea; sadness video clips were highlights of documentary films on people, whose lives were devoted to their neighbors. The video clips for anger were the news on people who abuse or fraud other people using their power. Orchestra videos were selected for relaxed.