Simple Coordination and Cognitive Stimulation Activities for Cognitive Function Assessments using Functional Near-Infrared Spectroscopy

From brain to worksite: the role of fNIRS in cognitive studies and worker safety

Effective hazard recognition and decision-making are crucial factors in ensuring workplace safety in the construction industry. Workers' cognition closely relates to that hazard-handling behavior. Functional near-infrared spectroscopy (fNIRS) is a neurotechique tool that can evaluate the concentration vibration of oxygenated hemoglobin [ H b O 2 ] and deoxygenated hemoglobin [H b R ] to reflect the cognition process. It is essential to monitor workers' brain activity by fNIRS to analyze their cognitive status and reveal the mechanism in hazard recognition and decision-making process, providing guidance for capability evaluation and management enhancement. This review offers a systematic assessment of fNIRS, encompassing the basic theory, experiment analysis, data analysis, and discussion. A literature search and content analysis are conducted to identify the application of fNIRS in construction safety research, the limitations of selected studies, and the prospects of fNIRS in future research. This article serves as a guide for researchers keen on harnessing fNIRS to bolster construction safety standards and forwards insightful recommendations for subsequent studies.

Nested and robust modeling techniques for fNIRS data with demographics and experiment related factors in n-back task

Functional near-infrared spectroscopy (fNIRS) signals are used to measure relative changes in oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentrations. Brain response studies constitute multilevel or nested datasets formed by different parts of the brain of individuals and multidimensional datasets. The changes in brain activities under specific stimuli are investigated with the help of statistical analysis. However, these studies ignore the dependence structure between the repeated measures of the same subject, which may cause inaccurate or incomplete findings. In this study, we adopt an advanced statistical method into HbO data controlling for variability within repeated measures of each subject while testing and measuring the degrees of the statistical significance between-subject factors and explanatory variables. The changes in HbO are investigated through a linear mixed model, taking experimental and demographic variables into account with open access neuroscience data. The channels nested within subjects are considered random to capture the differences among individuals. Our findings reveal that n-back conditions and mean response times of the subjects have statistically significant associations with mean HbO.