Neuronal optical response recognition based on chaos levels identification of near-infrared spectroscopy time series

Near-infrared spectroscopy (NIRS) can detect two different kinds of signals from the human brain: the hemodynamic (slow) optical response, and the neuronal (fast) optical response. This paper explores the nonlinear aspects of the tactile-stimulus-evoked neuronal optical response (fast optical response) over a NIRS time series (light intensity variation). The fast optical responses (FORs) over time series recorded in stimulus sessions are confirmed by event-related averaging. The chaos levels of NIRS time series recorded both in stimulus and in rest sessions are then identified according to the estimated largest Lyapunov exponent. The obtained results strongly suggest that the chaos level can be used to recognize the FORs in NIRS time series and, thereby, the state of the pertinent brain activity.