In the realm of human factors, objective assessment of cognitive states is crucial for the safe completion of tasks. Functional near-infrared spectroscopy (fNIRS) can be employed to recognize the cognitive activities and evaluate the mental workload associated with cognitive-executive processes. This study proposes a method for facilitating the faster detection of changes in cognitive states based on the correlation coefficients of adjacent channels, which enables the extraction of local connectivity (LC) features from fNIRS data. The results indicate that the extracted new features can reflect changes in the activation patterns of specific brain regions during the early stages (0~2.5s) of the task. It is suggested that these features could be used to identify the brain’s task states.