# A Photoplethysmogram Dataset for Emotional Analysis
## 4 Conclusions  
Emotions play a vital role in human communication, and the need for the function of automatically recognizing emotions using bio-signals is crucial in today's society; such functions can be used in various situations and for different purposes. With the help of technological advancements in recent years, automatic algorithms are capable of recognizing emotions from various bio-signals, especially via EEGs and ECGs. Recently, there has been interest in utilizing PPG for emotional status recognition, but the number of studies and datasets using PPG is limited in comparison to those utilizing the other signals. The main contribution of this study was the introduction of a new public dataset of PPG for emotional recognition. The dataset, collected from 18 participants, was proven to reflect the emotional states of the participants. This study aimed to introduce a dataset of PPG signals for emotional status analysis and recognition. The signals of the dataset were recorded from 18 participants, while each participant watched several preselected 3- to 4-min-long videos. The video clips were carefully selected to invoke emotions in the participants. Four types of videos were utilized in the experiment to respectively invoke sadness, relaxed, joy, and anger. The proposed data were analyzed in the time and frequency domains and the automatic classifications of the emotions were conducted with various artificial neural network structures. In the results, we found significant differences between different emotions in BPMs, and frequency and time-domain features. The BPM of anger was lower than joy and relaxed. The differences in frequency and time-domain features were observed personally. The VLFa of joy of a person was higher than relaxed; differences were found between anger and other emotions in meanNN, pNN40, SDNN, RMSSD, and meanNN. Moreover, we presented classification results with different network structures, achieving the best mean accuracy of 75.16% for arousal and 78.12% for valence. The main limitation of this study is the limited size of the dataset, as it contains 72 signals from 18 participants only. However, the proposed dataset will be useful for emotional analysis using PPG because it is one of a few PPG datasets and it is differentiated by the sorts of video stimuli. It could be utilized widely to evaluate algorithm performance, especially to test few-shots learnings. Conceptualization, W.-D.C. and S.P. ; Methodology, Y.-J.J.; Validation, Y.-J.J.; Investigation, Y.-J.J.; Data curation, Y.-S.J., T.A. and D.J. ; Writing--original draft preparation, E.H. and Y.-J.J.; Writing--review and editing, E.H. and W.-D.C.; Supervision, W.-D.C.; Project administration, W.-D.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding, and the APC was funded by brain Korea 21 program. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Year** & **Name** & **Signal Source** & **Number of Subjects** & **Type of Stimuli** & **Emotions** \\ \hline
[30] & 2012 & DEAP & EEG, EMG, EOG, GSR, PPG & 40 & Music video & Arousal, valence, liking, dominance \\
[13] & 2018 & WESAD & ECG, EMG, GSR, PPG, RSP & 15 & Music video, assessment & Neutral, stress, amusement \\
[14] & 2019 & CLAS & ECG, EDA **, PPG, GSR & 62 & Music video, picture & Arousal, valence, liking, dominance, control \\
[15] & 2019 & CASE & ECG, EMG, GSR, PPG, RSP & 30 & Movie clip & Four categories (amusing, boring, relaxing, and scary) \\
[16] & 2020 & K-EmoCon & ACC *, ECG, EDA **, EEG (1 channel), PPG & 32 & Debate & Arousal, valence, and 18 other categories \\ Proposed & 2022 & PPGE * & PPG & 18 & Narrative video & Four categories (joy, sadness, anger, and relaxed) \\ \hline \hline \end{tabular}
* ACC denotes 3-axis acceleration signal; PPGE is a shorten name of the proposed dataset. ** EDA denotes electrodermal activity.  
\end{table} 
Table 13: Physiological signal dataset with PPG for emotional analysis.