## 3 Methodology
### Temporal Convolutional Network Model with Channel Attention
#### 3.1.4 Model Analysis  
To validate the efficacy of the temporal convolutional network model with channel attention, this paper presents empirical evaluations using proprietary datasets collected across diverse emotional states. To guarantee the reliability of the results, a five-fold cross-validation method is employed. Given the limited range of emotion recognition techniques for PPG signals and the paucity of open-source implementations, this study also conducts comparative analyses with conventional methods and other prediction task models. Critical metrics for model comparison include accuracy, the ROC curve, and the AUC area under the ROC curve, all of which effectively capture the model's recognition prowess for learners' emotional states. The dataset used in the experiments consists of a multi-sensor collection in natural settings, where learning emotions are elicited through instructional videos designed to evoke varying emotions. The dataset in this article is part of the same batch of multimodal data collected as the dataset in our previous publications. The description of the experimental materials, subjects, and experimental process can be found in [31]. Participants were monitored using the Empatica E4 wristband to capture PPG signals during video watching. Following video viewings, participants completed survey questionnaires detailing their emotions. The induced emotion categories were subsequently determined based on survey responses and the actual context of the viewed videos. PPG signal recordings were then synchronized with the participants' survey data. PPG data from each video were segmented into 20 s chunks, and the dataset was annotated according to the induced emotional states. The research was conducted using Python and the Pytorch framework, with GPU acceleration to expedite computations. The experimental setup included an i9-11900K CPU and an Nvidia Geforce RTX 3080 GPU. (1) Analysis process  
Overall, the experiment is structured into three primary sections: PPG signal data preprocessing, neural network model construction, and model training. The initial section involves PPG signal data preprocessing, encompassing three main steps, as illustrated in Figure 3. This study selects emotional induction data from ten learners, with each learner exposed to 20 videos, resulting in 20 complete PPG signal data sets per learner. The preprocessing procedure comprises the following:  
1. Data time window alignment: The PPG signal data automatically recorded by each learner's 20 wristbands are stored in individual PPG signal files. The initial step involves aligning these data within a specified time window. While learners induce emotional data by watching videos, the experimenter records the video's playback and stop times. Based on these time stamps, each learner's PPG signal data are split and saved in CSV files, yielding 20 CSV files per learner containing the PPG signal data. 2. Data segmentation: Given the sampling frequency of 64 Hz, each CSV file is divided into 20 s segments. The PPG signal in each CSV file is subdivided into multiple segments and stored within the same CSV file. 3. Data annotation: In this step, PPG signals are annotated based on the scales completed by learners after watching the inducing videos and the labels of the emotional induction videos. All PPG signal data from the learners are collected and collated into a single large CSV file for subsequent experimental procedures. The primary data processing tools used are Numpy and Pandas from Python. The second stage involves model construction, predominantly implemented using Python and the Pytorch framework. Pytorch facilitates rapid development of custom neural network architectures with mature, robust technologies.