Cognitive load-strength estimation for NIRS with self-organizing particle filtering

In this paper, an algorithm to estimate instantaneous cognitive load-strength using near infrared spectroscopy(NIRS) is proposed. For the estimation of the cognitive load-strength, two factors derived from the conventional method with raw cerebral blood flow (rCBF) and from the frequency characteristics were considered. The latter focus on changes of the statistical properties in the rCBF signals based on the author's previous findings that the frequency bandwidth and characteristics of rCBF depend on cognitive load. For an instantaneous estimation of the statistical properties, a self-organizing particle filter was constructed, and the validity of the estimation of the instantaneous standard deviation of the rCBF was confirmed with dummy data. In the experiment, the cognitive load-strength estimation algorithm was verified by utilizing three levels mental computational tasks. Moreover, in order to adjust parameters in the algorithm, regression lines obtained from the difficulty levels and the estimated cognitive loads are analyzed, and an uncorrelated test was performed. As a result, the effectiveness of the proposed algorithm was confirmed that twelve out of thirteen participants were significantly difference (r = .39, N = 48, p = .006).