Cognitive Computational Neuroscience


Clinical applications: outcome prediction in coma patients

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.

In particular, in previous work we have used multivariate EEG decoding techniques to show, that patients who are deeply unconscious can detect at a neural level, violations of simple rules and complex sound sequences.

Auditory discrimination Tzovara et al., 2013, Brain, doi:10.1093/brain/aws264

Representative publications:

Computational and machine learning applications

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.

Single trial EEG analysis

Tzovara et al., 2012, Develop. Neuropsychology, doi: 110.1080/87565641.2011.636851

Reconstructing oscillatory activity from the hippocampus & amygdala using MEG

under construction

Modeling skin conductance & pupil responses

under construction

Representative publications