Infoscience

Thesis

Emotion Detection and Recognition based on Brain and Peripheral Physiological Signals

Emotions affect and determine social relationships and interactions, memory and creativity, and influence the mechanisms of rational thinking and decision making. The influence of emotion on decision making has gained attention in computer science. By detecting and recognizing emotions in an automatic way, machines endeavor to ease interaction between users and multimedia content. Automatic emotion detection and recognition can be carried out through analysis of users' various behavioural, and physiological signals such as electroencephalogram (EEG), electrocardiogram (ECG), electrodermal activity (EDA), and respiration, among others. These modalities have been extensively studied individually. However, since different individuals may experience the same emotion but express it differently, the various modalities are considered complementary, and fusion of various physiological and behavioural responses is expected to improve the quality of emotion recognition systems. Nevertheless, although representative features and their multimodal integration have been studied in affective computing research for various applications, patterns that arise from the dynamic interrelation among various modalities during emotional processes have received less attention. By summarizing each physiological signal only in a number of features, one may lose information present in the underlying dynamical co-evolution of various physiological signals. Considering all these issues, this thesis aims at detecting and recognizing emotion through brain and peripheral signals, targeting three complementary topics that have not been thoroughly explored. The first one includes emotion assessment from music video clips, the second one emotion assessment from odors, and the third one Quality of Experience (QoE) assessment from two-dimensional (2D) and three-dimensional (3D) video contents, using in all cases brain and peripheral physiological signals. Regarding emotion assessment from music video clips, subject-dependent and subject-independent analyses are carried out in this thesis, and the results reveal that although there are differentiations among the subjects' brain activation patterns, there are still common patterns across them. Moreover, the dynamical co-evolution between EEG and EDA is explored during emotional processes, and the results reveal that the coupling between EDA and EEG of the temporal lobe increases when strong emotions occur with respect to neutral ones. Finally, possible clustering patterns across subject-categories are investigated, and the results reveal that there are common characteristics across subject-categories related to their emotions. Regarding emotion assessment from odors, since the primary response to odors is related to pleasantness perception, which has not yet been thoroughly investigated, this thesis explores the way perceived odor pleasantness influences brain and periphery. In particular, two independent classifiers are trained and tested, one using EEG and the other using ECG features. The results reveal that it is possible to assess odor pleasantness perception from EEG and less accurately from ECG features, in a subject-independent framework. Also, decision fusion of the EEG and ECG classifiers is shown to discriminate odor pleasantness perception. Moreover, in order to explore the dynamical co-evolution between brain and peripheral signals, the coupling between heart rate and EEG is investigated. The results reveal that there is a significant increase in the coupling between ECG and temporal lobe EEG, when pleasant or unpleasant odors are experienced with respect to neutral ones. Enhanced QoE from multimedia contents targets to increase users' sensation of reality, in order to induce stronger emotions and render the user more involved in the experience. In this thesis, QoE is investigated in terms of four aspects, namely perceived depth, perceived overall quality, content preference, and sensation of reality. In particular, it is revealed that it is possible to recognize perceived depth, content preference, and sensation of reality from EEG signals, but not from the peripheral ones. Also, fusion between peripheral and EEG features is found to improve the performance in some cases. Finally, the left frontal cortex seems to be activated when sensation of reality is high, indicating that high sensation of reality is related to approach-related emotional processes. Although the three topics are independently explored in this thesis, their potential integration would endeavor to create immersive multimedia systems, which could adapt their properties according to users' emotions, and enhance, thus, user-experience.

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