Grant: Discovery Grants Program - Individual

Computerized interfaces (e.g., smartphones) are now omnipresent and new technologies will continue to transform our lives in a similar fashion (e.g., wearables, gestural interfaces). What a user experiences when interacting with such interfaces is a key determinant of his/her intention to use and/or reuse a product or service: research shows that the user experience, or UX, plays an important role in the success or failure of digital products and services. In recent years, there have been several calls for research to investigate how the use of neurophysiological data can help us reach a better understanding of human behavior while interacting with computerized interfaces. By neurophysiological data, we refer to the various type of data collected on individuals using techniques of cognitive neurosciences for studying the reactions of either the central nervous systems (e.g. electroencephalography) or the peripheral nervous systems (e.g. galvanic skin response or oculometry). My research program thus aims to lead research projects that will provide guidelines and propose new statistical models constructed explicitly for the analysis of neurophysiological data. These new models will thus increase the chances of finding new results and gain a better understanding of human-computer interactions. This research program is divided in two parts. First, I will propose new statistical models as well as new estimation, forecasting and segmentation procedures for multivariate neurophysiological data. The second part is more specific as I will propose and study new models specifically for electroencephalography (EEG) data that are more appropriate when the number of trials/events observed is small or moderate, as well as develop new sample size determination techniques. These statistical advances will then open new research opportunities and the possibility to address unexplored research questions. This will help us capture more efficiently the user's affective, cognitive, and attentional states throughout his/her experience. Thus increasing the chances of finding new results and advance knowledge in UX, and other fields relying on the analysis of neurophysiological data.