Development of motion artifact removal algorithm and cerebral autoregulation evaluation method for portable functional near infrared spectroscopy

Abstract

Functional near-infrared spectroscopy (fNIRS) has been widely utilized for non-invasive neuroimaging of the brain in cognitive research that measures the temporal changes in oxy- and deoxy-hemoglobin concentrations. Due to improvements in portability and resolution, fNIRS research has begun to expand towards dynamic applications such as neuro-rehabilitation, neuro-feedback, and brain-computer interface technology. However, the intrinsic vulnerability of fNIRS to motion artifacts (MA) reduces measurement accuracy, which in turn necessitates complex post-processing. In the present study, we demonstrated that our proposed real-time motion sensor-based algorithm for motion artifact compensation (RT-MAC), which extracts MAs based on hemodynamic modeling of an individual’s cerebrovascular system, is effective for real-time brain imaging applications. In the use case of clinical applications with fNIRS system we present a direct assessment method of cerebral autoregulation (CA) using a multichannel continuous-wave near-infrared spectroscopy (CW-NIRS) device in the prefrontal cortex. We introduce a new metric termed ‘time-derivative hemodynamic model (DHbT)’, which is the time-derivative of total-hemoglobin concentration change that reflects the changes of cerebral blood volume and cerebral blood flow. Although the absolute levels and the variations of systolic and diastolic BPs and mean arterial pressure showed no significant difference between subjects with frequent symptom of orthostatic intolerance and the healthy control subjects, the proposed model showed a distinct difference in slope variation and response time of DHbT between the two groups. Thus, these results clearly demonstrate the feasibility of using CW-NIRS devices as a CA performance assessment tool.