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Updated: May 19, 2026
Reducing State Anxiety Using Working Memory Maintenance
Published on: July 19, 2017
Bayesian STAI anxiety index predictions based on prefrontal cortex NIRS data for the resting state
Masakaze Sato1, Wakana Ishikawa2, Tomohiko Suzuki2
1Department of Electrical Engineering and Bioscience, Waseda University, 3-4-1 Kasumigaoka, Lions Garden Kamifukuoka #801, Fujimono-si, Saitama, 356-006, Japan. sato09@matsumoto.eb.waseda.ac.jp.
Abstract:
Several distinctive activity patterns have been observed in the brain at rest. The aim of this study was to determine whether the STAI index can be predicted from changes in the oxy- and deoxy-hemoglobin (Hb) concentrations by using two-channel prefrontal cortex (PFC) NIRS data for the resting state. The study population comprised 19 subjects. Each subject performed four trials, each of which consisted of resting with no task for 3 min. Data were acquired using a portable NIRS device equipped with two channels. The prediction algorithm was derived within a Bayesian machine learning framework. The prediction errors for seven subjects were not greater than 5.0. Because the STAI index varied between 20 and 80, these predictions appeared reasonable. The present method allowed prediction of mental status based on the NIRS data at resting condition obtained in the PFC.
