**3. Methods**

To evaluate the classification accuracy in ER from physiological signals, we adopted the two dimensional Valence/Arousal space. As previously mentioned, the ECG, RESP, EDA, and BVP signals are used, and we compare FF and DF techniques in a feature space based framework. In the forthcoming sub-sections, a more detailed description of each approach is presented.

#### *3.1. Feature Fusion*

As previously mentioned, when working with multi-modal approaches the exploitation of the different signal modalities can be performed resorting to different techniques. We start by testing the FF technique. In FF, the features are independently extracted from each sensor modality (in our case ECG, BVP, EDA, and RESP), and are concatenated afterwards to form a single, global, feature vector (570 features for EDA, 373 for ECG, 322 for BVP, and 487 for RESP, implemented and detailed in the BioSPPy software library https://github.com/PIA-Group/BioSPPy). Additionally, we applied sequential forward feature selection (SFFS) in order to preserve only the most informative features, and save time and computational power of the machine learning algorithm to be applied in the next step. All the presented methods were implemented in Python and made available as open source software https://github.com/PIA-Group/BioSPPy.

#### *3.2. Decision Fusion*

In contrast to FF, in DF, from each sensor signal, a feature vector is extracted and used independently to train and learn a classifier, so that each modality returns a set of predicted labels. Hence, with *k* modalities, *k* classifiers will be created returning *k* predictions per sample. The returned predictions are then combined to yield a final result, in our case, via a weighted majority voting system. In this voting system, the ensemble decides on the class that receives the highest number of votes taking into account all sensor modalities, and a weight ( *W*) parameter per modality to give the more competent classifiers a greater power for the final decision. The weights were chosen for each modality according to the classifier accuracy on the validation set. In case of a draw in the class prediction, the selection is random.
