Abstract

In this paper, we investigate a real-time analysis of NIRS data by using an unsupervised Gaussian mixture model adaptive classifier (GMMAC) for a framework consisting of the general linear model (GLM) and the Kalman estimator to improve decoding accuracy. Simulation experiment results demonstrate that the GMMAC (91.9%) perform significantly better than linear discriminant analysis (LDA, 51.9%) and support vector machines (SVM, 51.1%) classifiers in binary problems.

© 2018 The Author(s)