Tensor Decomposition of Functional Near-Infrared Spectroscopy (fNIRS) Signals for Pattern Discovery of Cognitive Response in Infants

Abstract

Functional near-infrared spectroscopy (fNIRS) provides an effective tool in neuroscience studies of cognition in infants. fNIRS signals are normally processed by applying ANOVA analysis on the grand average of the hemodynamic responses to investigate the cognitive-related differences between experimental groups. However, this averaging approach does not account for any differences in the temporal patterns of the responses. Therefore, we propose a new approach based on a combination of tensor decomposition and ANOVA. First, a four-way tensor of the hemodynamic responses is arranged as time × frequency × channel× subject and decomposed using Canonical Polyadic Decomposition (CPD). Next, ANOVA is applied to identify significant patterns between subjects. Instead of averaging, the CPD can capture the distinct patterns between groups in all the dimensions. We used fNIRS dataset of 70 infants who participated in an experiment to investigate cortical activation to an agent (i.e., mechanical claws vs. human hands) with different events (i.e., function and non-function). In the comparison with the traditional ANOVA, CPD+ANOVA identified the same significance factors. However, CPD+ANOVA discovered new information on the temporal and spatial patterns indicating a longer interval hemodynamic responses, which was missed using the traditional ANOVA. This new analysis of hemodynamic responses as captured using fNIRS will improve neuroscience and cognitive studies.