A Compressive Sensing Spectral Model for fNIRS Haemodynamic Response De-Noising

In the biomedical scenario, near-infrared spectroscopy (NIRS) is employed as a non-invasive brain imaging technique. In particular, functional near-infrared spectroscopy (fNIRS) provides the neural evoked response, also known as haemodynamic response (HR), to pre-defined stimuli. Processing of fNIRS data requires a great effort to extrapolate the informative component from a noisy mixture of physiological and spurious contributions. In this paper a novel fNIRS de-noising algorithm is presented and validated over both simulation and experimental data. For each evoked response, a specific noise model is identified and subtracted from the acquired data. The algorithm relies on a combination of a super-resolution technique based on Compressive Sensing theory and a spectral analysis performed via Taylor-Fourier transform. Preliminary experimental results show a significant reduction of spurious components in all the considered conditions. No significant distortions are introduced in the recovered HR, ensuring reliable clinical interpretation of the acquired trace