Title: A novel framework for neuroscience and EEG event-related potentials with virtual reality and deep autoencoders
Description: Electroencephalography (EEG) and its applications within Human-Computer Interaction and for brain-computer interface development (BCI) is increasing. Within neuroscience, there is an increasing emphasis on the analysis of event related potentials (ERP) for brain functioning in ecological contexts rather than exclusively in lab-constrained environments. This is because humans might not behave naturally in controlled settings, thus influencing the reliability of findings. However, EEG studies performed in natural/ecological settings are more problematic than in controlled settings, because the control of EEG equipment by researchers is inferior. For these reasons, a new trend is devoted to the application of Virtual Reality (VR) in the context of ERP research, for the development of ecological virtual environments similar to real ones. The advantage is that a traditional ERP study can still be performed in supervised settings, but giving the researcher a full control over experimental factors and EEG equipment. This PhD will produce a novel framework that will allow scholars to perform ERP-based research in ecological settings, by employing VR, and by constructing autoencoders, fully unsupervised deep-learning methods for automatic EEG artefact reduction, by taking advantage of not only temporal dynamics of EEG, but also their spatial and frequency-domain characteristics.