Phase component of frequency-domain functional near-infrared imaging improves decoding of motor-evoked neural activity

Frequency-domain functional near-infrared spectroscopy (FD-fNIRS) has the potential to improve neural imaging of brain hemodynamic responses over conventional magnitude measurements from continuous wave (CW) fNIRS systems by providing additional measurement of phase changes. We evaluated whether phase measurements improved accuracy in decoding motor activity and laterality of movement while recording from motor cortex during a finger tapping task conducted with N=12 subjects. Classification was performed using logistic regression with a single feature derived from hemodynamic response function (HRF) regression. Inspecting the regression results on held-out test data, the majority of subjects showed significant differences between baseline and activity conditions over a typical HRF time course in both magnitude and phase signal components. Combining magnitude and phase information using FD-fNIRS significantly improved classification accuracy of motor conditions at the population level relative to the CW-fNIRS analogue represented by the magnitude signal alone. Our results demonstrate that FD-fNIRS systems can provide benefit over CW-fNIRS for neural decoding applications and are a promising technology for future investigation of non-invasive neural imaging.