My research focuses on the use of computational and machine learning techniques to study neural functions of the human brain, in healthy and pathological conditions. The main axes of my research lay in three areas:
In our day-to-day lives, we are constantly immersed in streams of sensory information. Stimuli from our environment, like for example sounds, very often do not occur in a random way, but follow statistical rules and repetitive patterns. Thanks to these rules we are able to form expectations about future events, before these occur. I am interested in studying the neural and computational mechanisms that allow us to detect violations of anticipated events, in relation to our levels of consciousness, attention, or complexity of the environment.
We have been using EEG recordings in coma patients to study neural functions in the absence of consciousness and, at the same time, to establish predictors of their outcome. In particular, we have applied multivariate EEG decoding techniques to analyze EEG responses at the single patient level. We were able to show that even patients who are deeply unconscious can detect at a neural level, violations of simple rules and complex sound sequences. Importantly, the progression of auditory discrimination, quantified by the level of decoding performance over the two first days of coma, was informative of the patients’ chances of awakening:
Tzovara et al., 2013, Brain, doi:10.1093/brain/aws264
Studying neural responses to environmental stimuli through electroencephalography (EEG), typically requires averaging hundrents or thousands of single-trial responses, and contrasting them at single electrode locations. During my PhD, I used multivariate techniques to model the distribution of single-trial EEG responses across the scalp, and extract topographic EEG responses in a data-driven way. This technique can be used to model data at the single-patient level, to decode decisions from EEG responses, or to test the role of temporal intervals in processing environmental stimuli.
Tzovara et al., 2012, Develop. Neuropsychology, doi: 110.1080/87565641.2011.636851