## Appendix B BioVid Heat Pain Database Part A  
The BioVid Heat Pain Database (BVDB) [35] was collected at Ulm University to enhance the research in the field of machine learning-based emotion- and pain (intensity) recognition. The publicly available--strictly restricted to research purposes--BVDB consists of five parts ([[http://www.iikt.ovgu.de/BioVid.print](http://www.iikt.ovgu.de/BioVid.print)]([http://www.iikt.ovgu.de/BioVid.print](http://www.iikt.ovgu.de/BioVid.print)), last access on 9 February 2022). In the current study, we focus on _Part A_ of the BVDB, i.e., by the abbreviation BVDB we will always refer to Part A of the database.  
A total amount of 87 healthy test subjects participated in strictly controlled pain elicitation experiments that were conducted with a Medoc heat thermode ([[https://www.medoc-web.com/](https://www.medoc-web.com/)]([https://www.medoc-web.com/](https://www.medoc-web.com/)), last access on 9 February 2022), which was attached at one of the participant's forearms. Each participant had to undergo an individual calibration phase, which led to four equidistant temperature values, i.e., pain levels. To avoid skin burns, it was strictly forbidden to exceed the temperature of 50.5 \({}^{\circ}\)C.  
Each of the participants was stimulated 20 times with each of the four pain levels in randomised order. Each pain level was held for 4 s. Between two pain level-specific stimuli, the temperature was linearly decreased to 32 \({}^{\circ}\)C, denoted as baseline, and held for a random duration of 8-12 s. During the experiments, the participants were recorded from three different angles, leading to three video signals. Additionally, the experimenters recorded the following three physiological signals, electrocardiogram (ECG), electromyogram (EMG), and electrodermal activity (EDA).  
In the current work, we focus on the physiological modalities. The ECG signals measure a person's heart activity. The EMG signals measure a person's muscle activity. The EMG sensor was attached to the trapezius muscle (in Part A of the BVDB), which is located at the back of a human torso, in the shoulder area. The EDA signals measure a person's skin conductance. To this end, the sensors were attached at the participant's ring and index finger, respectively.  
Note that each of the physiological signals constitute a time series. To manually extract features, windows of length 5.5 s were defined and applied to each of the pain-related and baseline stimuli. Different statistical descriptors were extracted from the frequency domain, including _mean_, _min_, and _max_, among others. Additionally, different descriptors were extracted from the temporal domain, including _mean_, _min_, and _max_, among others. Moreover, for the ECG modality, different signal-specific features were extracted that are based on the so-called Q, P, R, S, and T wavelets. As the process of feature extraction is not part of the current contribution, we refer the reader to [36] or [37] for a detailed feature extraction analysis, because we are using exactly the same features in the current work.  
The feature extraction process led to a total of 194 features, including 56, 68, and 70 features for the modalities EMG, ECG, and EDA, respectively. Following the feature extraction step, the participant-specific feature subsets were normalised leading to the values 0 and 1, for the mean and standard variation, respectively.  
Note that the BVDB constitutes an ordinal data set in the traditional way, specific to the class label order _no pain \(\prec\) low pain \(\prec\) intermediate pain \(\prec\) strong pain \(\prec\) unbearable pain_. While this data set is usually not evaluated in combination with its corresponding class order, recently, we showed the effectiveness of focusing on the ordinal structure in [1].