*3.1. MAHNOB*

The dataset used is called MAHNOB and was collected by Soleymani Mohammad et al. [31]. The data is related to different physiological signals.

The data was collected from 30 young healthy adults who participated in the study. 17 of the participants were female and 13 of them were males. Their age varied between 19 to 40. The participants were shown 20 emotional video clips which were evaluated in terms of both valence and arousal by using the Self-Assessment Manikins (SAM) questionnaire [32]. SAM is a prominent tool that visualizes the degree of valence and arousal by manikins. The participants distinguished a scale from 1 to 9, see Figure 1.

**Figure 1.** Self-assessment manikins scales for valence (above) and arousal (below) [32].

In the experiments for MAHNOB, electroencephalogram (EEG), blood volume pressure (BVP), respiration pattern, skin temperature, electromyogram (EMG), electrooculogram (EOG), electrocardiogram (ECG), and EDA of 30 participants were collected.

## *3.2. DEAP*

DEAP [33] is a multimodal dataset used to analyze human emotional states.

The stimuli used in the experiments were chosen in different steps. First, they selected 120 initial stimuli that were selected both semi-automatically and manually. Second, a one-minute highlight part was specified for each stimulus. Third, through a web-based subjective assessment experiment, 40 final stimuli were chosen.

During the physiological experiment, 32 participants evaluated 40 videos via a web interface used for subjective emotion assessment in terms of the levels of arousal, valence, like/dislike, dominance, and familiarity. The age of participants varied between 19 to 37. Concerning the classes/labels for DEAP, we considered the same classes as same as in Section 4.1.

In the experiment, electroencephalogram (EEG), BVP, respiration pattern, ST, electromyogram (EMG), electrooculogram (EOG), electrocardiogram (ECG), and EDA of 32 participants were collected.

#### **4. Classification Using a Convolution Neural Network—CNN**

In this section, we present, the labelling of EDA signals, the design details of the proposed CNN for emotion classification and then, the evaluation metrics and evaluation.

#### *4.1. Preprocessing and Labelling*

First, raw data of EDA were scaled such that the distribution is centered around 0, with a standard deviation of 1. Additionally, after data normalization, two states [34] valence and arousal are addressed for emotion classification. In this regard, the scales (1–9) were mapped into 2 levels for each valence and arousal state according to the SAM ratings.

The valence scale of 1–5 was mapped to "negative" and 6–9 to "positive", respectively. The arousal scale of 1–5 was mapped to "passive" and 6–9 to "active", respectively.

