Emotion recognition in Virtual Reality(VR) has the potential to offer numerous benefits across various sectors such as mental healthcare, education, marketing, entertainment, etc. Although emotion recognition itself is a mature field, the sub-field of VR-based emotion recognition is still in its early stages of development. It was found that a limiting factor in the progress of this field is a lack of sufficient data for research and development of advanced deep learning models. Also, the equipment currently used to measure emotion-related signals is expensive and impractical for general usage. This thesis aims to support the progress in this field by creating a VR-based emotion recognition dataset using VR equipment only. This addresses the problem of insufficient data available for research and development, and also reduces the reliance on expensive and impractical equipment for emotion recognition.

To create a good quality dataset, several important things had to be addressed. First of all, the stimuli to evoke the emotions had to be carefully selected to ensure that genuine emotional responses were evoked and recorded in the dataset. Then, an efficient data collection system had to be created to ensure that the data collection process ran effectively, smoothly and consistently. Then, a proper labeling process had to be designed to annotate the data as accurately as possible. Finally, the compiled dataset was validated by showing that the chosen stimuli were effective in evoking the intended emotions. This was verified through the analysis of pupil response data, which is one of the recorded data modalities.

This thesis describes the digital implementation of a motional feedback system for a bass loudspeaker. Motional feedback is used to suppress the linear and non-linear distortions produced by the loudspeaker, especially at the low frequencies. An accelerometer is mounted on the cone of the loudspeaker to provide the feedback signal. The controller which consists of a PI controller and an equalizer are implemented on an FPGA. The equalizer, which is the inverse of the linear model of the loudspeaker, is used to compensate for the linear distortion. The PI controller with negative feedback is used to suppress the non-linear distortion. Not all measurement results are available at the moment of submission of this thesis. However, simulations were carried out on the model of the loudspeakers which show that the linear distortion is fully suppressed. The reduction of the non-linear distortion due to the controller can not be seen in the simulations.