Recognition of emotional and cognitive states using physiological data

This thesis presents the application of several pattern recognition techniques on physiological data as a means to provide useful information about human emotional or cognitive states. As these states may be correlated with the well-being and performance of subjects, knowledge of these states could improve the human-computer interaction, increase productivity, and reduce accidents. We first focus on a method for recognizing the emotional state of a person who is deliberately expressing one of eight emotions. Four physiological signals were measured and six features of each of these signals were extracted. We investigated three methods for the recognition: (1) Sequential floating forward search (SFFS) feature selection with K-nearest neighbors classification, (2) Fisher Projection (FP) on structured subsets of features with MAP classification, and (3) A hybrid SFFS-FP method. Each method was evaluated on the full set of eight emotions as well as on several subsets. The day-to-day varia...