Brain function evaluation using enhanced fNIRS signals extraction

fNIRS is widely used to monitor brain activities, however the measured hemodynamic signals might not reflect the true cortical events due to systemic signal fluctuations. These signals include several types of noise spread from low to high frequencies such as respiratory interference frequency band of 0.1-0.3Hz, NIRS Mayer wave which is about 0.1 Hz, cardiac interference frequency band which is 0.8-2.0 Hz, artifacts from head and facial motions, and high frequency noise generated from electronic components. The Hemodynamic Evoked Response (HomER) graphical user interface is used to display the NIRS data, FastICA is updated to reduce data dimension and combined Wavelet & PCA method is developed to denoise NIRS signals. The applied processing technique consists of adaptively modifying the wavelet coefficients based on the degree of noise contaminating the processed NIRS signal. This is done subsequently to signal pre-processing by reducing data dimension using the FastICA method. The feasibility of the method was demonstrated by testing it on experimental fNIRS data collected from 47 subjects. Preliminary results, through signal-to-noise ratio and correlation indicators show more efficiency to reduce noise and improve the quality of the acquired fNIRS signals than those resulting from conventional fNIRS signal denoising.