Towards the Identification of Psychophysiological States in EEG

The mechanisms that regulate our physiological and mental processes behave in a coupled way in which there is a notorious interdependency. Mental processes are responsible for changes in the physiological state of our body. On the other side, changes in bodily functions also lead to different thoughts, behaviours and emotions. Boosted by the development of technologies such as virtual reality and wearable devices, understanding the physiological responses to emotional states can serve a wide range of valuable applications in such diverse fields as medicine and eHealth, education, entertainment or marketing. One of the main goals of this dissertation was to develop a system for emotion recognition in EEG signals. A typical classification pipeline was followed: after a preprocessing stage, a range of features were extracted from the signals. The most important features for discriminating the four target emotions (joy, calmness, sadness and fear) were selected using the ReliefF algorithm. These were fed to a SVM classifier with a RBF kernel. The proposed methodology was tested in a publicly available dataset (DEAP). Although a direct comparison with the state-of-the-art is difficult due to myriad of approaches followed by different authors, the high accuracy of 78,71% obtained in this dissertation reveals a better performance than some of the works in the literature. Furthermore, the same methodology proved to be robust and efficient in new data, recorded in different conditions. The second part of this work was focused on the influence of the modality of visual stimuli in the perception and expression of the same set of emotions. Therefore, a stimulation protocol was designed so as to elicit specific emotional responses with images, 2D videos and spherical videos in a virtual reality environment. The participants rated their experience using the SAM scale, representing valence and arousal. 17 volunteers took part in this study. The EEG was recorded during the experiment for 8 of them. When analysing the ratings given by the participants, only some one-off differences were found between modalities. Overall, the results did not reveal any predominant effect from any of the modalities. However, the exploratory analysis conducted to investigate the power of the Alpha, Beta and Theta frequency bands indicated that the virtual reality videos had a significant impact on the participants’ brain activity. This was particularly noticeable for the high-arousal emotions, which shows that the effects of this emotional dimension, despite being harder to recognize and express, can be captured in the EEG signal. That is, EEG features can capture emotional responses which can not be expressed in the form of a rating. In spite of the obstacles in such an intrinsically subjective topic as are emotions, the work developed in this dissertation validates the use of physiological signals to identify emotional states with encouraging results. These results also point to the benefits of using virtual reality in contexts where emotion is of paramount importance and indicate a line of research which will surely bear fruit in real applications.