Affective computing is an emerging discipline that aims to develop systems and devices capable of recognizing, interpreting, processing, and stimulating human emotions. This paper presents an extended version of the work presented at CACIC 2024, considering the background in terms of instruments, methods, and models in the field of affective computing. It describes a developed multimodal emotional framework that uses data from physiological sensors (heart rate and skin conductance) and facial expressions to predict a subject's emotional state. Additionally, a BCI (Brain-Computer Interface) is used to obtain relaxation and attention. Finally, the general results of experiments conducted in a static environment using IAPS images and in a dynamic environment using a flight simulator are presented and discussed.