  In order to develop more precise and functional affective applications, it is
necessary to achieve a balance between the psychology and the engineering
applied to emotions. Signals from the central and peripheral nervous systems
have been used for emotion recognition purposes, however, their operation and
the relationship between them remains unknown. In this context, in the present
work we have tried to approach the study of the psychobiology of both systems
in order to generate a computational model for the recognition of emotions in
the dimension of valence. To this end, the electroencephalography (EEG) signal,
electrocardiography (ECG) signal and skin temperature of 24 subjects have been
studied. Each methodology has been evaluated individually, finding
characteristic patterns of positive and negative emotions in each of them.
After feature selection of each methodology, the results of the classification
showed that, although the classification of emotions is possible at both
central and peripheral levels, the multimodal approach did not improve the
results obtained through the EEG alone. In addition, differences have been
observed between cerebral and physiological responses in the processing
emotions by separating the sample by sex; though, the differences between men
and women were only notable at the physiological level.
