Abstract:
In recent years, positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have facilitated the monitoring of the human brain non-invasively, during functional activity. Nevertheless, the use of these systems remain limited since they are expensive, they cannot provide sufficient temporal detail and they are not very comfortable for the patient or the volunteer whose brain is monitored. Functional near infrared spectroscopy (fNIRS), on the other hand, is an emerging non-invasive modality which may be a remedy for the failures of the existing technologies. However, properly designed data analysis schemes for fNIRS have been missing. In this M.S. thesis, we intend to introduce a collection of signal processing methods in order to treat fNIRS data acquired during functional activity of the human brain. Along extensive hypothesis tests that characterized the statistical properties of the empirical data, we have described the signals in the time-frequency plane and partitioned the signal spectrum into several dissimilar subbands using an hierarchical clustering procedure. The proposed subband partitioning scheme is original and can easily be applied to signals other than fNIRS. In addition to these, we have adapted two different exploratory data analysis tools, namely, independent component analysis (ICA) and waveform clustering, to fNIRS short-time signals in order to learn generic cognitive activity-related waveforms, which are the counterparts of the brain hemodynamic response in fMRI. The periodicity analysis of the signals in the 30-250 mHz range validates that fNIRS measures indeed functional cognitive activity. Furthermore, as extensive ICA and waveform clustering experiments put into evidence, cognitive activity measured by fNIRS, reveals itself in a way very similar to the one measured by fMRI. These findings indicate that, in the near future, fNIRS shall play a more important role in explaining cognitive activity of the human brain.
