Investigation of regions of interest (ROI) through the selection of optimized channels in fNIRS data

Abstract

We have proposed a method for extracting optimal regions of interest (ROI) through the selection of optimized channels, using machine learning classifiers and genetic algorithms in relation to functional near-infrared spectroscopy (fNIRS) data. Classifiers in machine learning have been used for determining labels belonging to test data. By using classifiers in the proposed method when determining object functions through optimization of existing discriminant functions, identifying the brain function area related to a particular subject is possible. In feature extraction, dynamic time warping (DTW) is used to extract any similarity in fNIRS data, and brain function areas are identified for a certain subject through classification by the support vector machine and feature extraction using the genetic algorithm. We confirmed the extraction of the areas related to working memory and results related to the brain function network by applying the proposed method to a time series of cerebral blood flow during a reading span test.