A No-Reference Metric of Cerebral Blood Flow Extraction for fNIRS Data

Functional near-infrared spectroscopy (fNIRS) offers a convenient means to measure human brain activity in real environments, driving many applications in neuroscience, medicine, and engineering. However, it is well known that fNIRS observations are always contaminated by scalp blood flow (SBF), which is not from the brain but from the skin. Several methods have been proposed for extracting cerebral blood flow (CBF), which reflects brain activities. The issue we tackled in this paper is how to evaluate the quality of CBF extraction for designing effective fNIRS applications. The standard way is to simultaneously measure reference signals such as SBF by laser Doppler blood flow meters and compare extracted CBF with them. This hardware approach requires additional instruments to be installed on the fNIRS device, declining the convenience of fNIRS recording in real environments. Then, in this paper we proposed a no-reference metric for evaluating the performance of CBF extraction. The proposed metric is based on decoding analysis of only fNIRS data and does not require additional instruments. The fundamental idea is to utilize the difference of SBF patterns in two experimental conditions; therefore an inherent limitation of the proposed metric is that it does not directly apply to single-condition data. We tested the proposed metric with popular CBF extraction techniques, namely, principal component analysis (PCA) and independent component analysis (ICA), and observed that these techniques may have room for improvement. The no-reference metric would be useful for evaluating CBF extraction techniques and promote the development of fNIRS applications.