Automatic emotion recognition represents a complex challenge in affective computing, hindered by the unreliability of self-reported assessments and the limitations of conventional biosignal processing techniques. This thesis aims to overcome these limitations by developing a robust framework for continuously predicting emotional arousal and valence. The framework is validated on a newly acquired multimodal dataset, collected for this study, which includes photoplethysmographic (PPG), galvanic skin response (GSR), pupillometry, and facial expression (FER) data, recorded concurrently with participant self-ratings during emotional video stimulation. This research presents three key contributions: the Python implementation of the INDSCAL algorithm, used to derive a reliable, individualized ground truth from the collected subjective ratings; an in-depth, comparative analysis of Heart Rate Variability (HRV) using advanced feature extraction; and the development of a multimodal fusion framework for continuous prediction of emotional dimensions.