# A Photoplethysmogram Dataset for Emotional Analysis
## 1 Introduction  
Emotion is a critical factor in human communication. In certain conditions, the same words or sentences can have different meanings according to the emotional state of a person, and individuals often try to please other people and assess their responses in various ways [1]. Likewise, emotions are also considered important factors for human-computer interactions (HCI) [2, 3]. Some robots are designed to express emotions with eye movements [4, 5], while certain machines are aimed at recognizing people's emotions using facial images and bio-signals [6, 7]. Automatic recognition of emotions using bio-signals can be utilized for health monitoring. It can be used to monitor patients in intensive care units [8, 9] and to protect workers in extreme conditions (e.g., firefighters and soldiers) [10, 11]. Emotional recognition using electrocardiograms (ECGs) has been studied by many research groups and verified by various databases [12, 13, 14, 15, 16, 17, 18, 19, 20, 21], achieving accuracies higher than 95% when classifying emotions into four classes. Notably, the accuracy of ECGs is high enough for most applications, but it can be uncomfortable and bothersome to attach electric sensors to the chest to measure a clear ECG. Studies have been conducted to recognize emotional status using electroencephalograms (EEGs). [PERSON] et al. presented a method to classify emotional states using linear regression and the AdaBoost algorithm [22]. They achieved a 66.03% accuracy for the binary classification of low and high valence and a 68.68% accuracy for arousal classification. [PERSON] et al. presented a method to recognize emotional states from 30-channel EEG signals using a support vector machine [23]. They extracted spectral power fromfrequency bands, and classified four emotional states (joy, anger, sadness, and pleasure) with an accuracy of 82.29%. Recently, [PERSON] et al. achieved an accuracy of 89.33% in classifying nine emotional states using 32-channel EEGs [7]. Recent advances in emotional recognition using EEGs have been described in a review paper by [PERSON] et al. [24]. The best accuracy in the literature was 98.37% for binary classification of emotions (positive and negative) [25]. Photoplethysmogram (PPG), on the other hand, is a bio-signal that contains information about a person's emotional status. The biggest advantage of PPG in comparison to other bio-signals is its ease of data acquisition. PPG is performed with an inexpensive and small sensor that is composed of two light emitting diodes (LEDs) and one optical sensor. The price of the sensor is relatively low at less than $30 in online marketplaces, and the signals can be recorded with a common single-board microcontroller, such as Arduino [26]. PPG signals can also be recorded using a smartphone [27]. The relationships between emotional conditions and PPG signal changes have recently been studied by several research groups [8,14]. [PERSON] and [PERSON] reported that the proportion of high frequency (HF), low frequency (LF), and very low frequency (VLF) signals changes according to emotional conditions based on a PPG study of nine participants [28]. The direction of the PPG changes varied according to the participants, and they were not able to identify general principles that explained these findings. The practicability of utilizing PPG to recognize emotional conditions automatically is still vague because sufficient studies have not been conducted. There have been a few studies on emotional recognition using PPG signals. [PERSON] et al. introduced a method of utilizing convolutional neural networks and achieved results of 82.1% and 80.9% for binary recognition of valence and arousal, respectively [29]. Despite its high accuracy, the method partly relies on the division of the training and test datasets. The most popular dataset for PPG-based emotional recognition is the DEAP dataset [30]. This dataset consists of multiple physiological signals (including EEGs and PPG signals) from 32 participants.