Complex wavelets applied to diffuse optical spectroscopy for brain activity detection.

The analysis of diffuse optical imaging (DOI) data has seen significant developments over the last few years. When compared to fMRI, signals originating from optical imaging are tainted by more physiology and the separation of activation from this background can be difficult in some cases. In this work, we show that the use of time-frequency techniques based on wavelets distinguish different physiological sources from the evoked response to a given stimulus. In particular, we show that analytical complex wavelets identify synchronies in the signal at different scales. These synchronies are then used to extract activation information from the DOI data in order to estimate the evoked hemodynamic response or to define a new type of contrast between two conditions. This work presents both simulations and applications with real data (visual stimulation and motor tasks experiments).