Abstract
Fuzzy c-Regression Models (FCRM) is a fuzzy clustering-based switching regression method and can be used for pattern classification. In this paper, a hybrid approach to FCRM and category partitioning is applied to physiological measurement data for brain activity analysis, which is a physiological measurement approach to qualitative modeling of Kansei information based on measurement of brain functions. A task prediction problem is formulated by using time series of oxidized hemoglobin measured by Near-infrared spectroscopy (NIRS), and FCRM with partitioning of categories is used for revealing mutual relation between a particular task and external factors represented by categorical observations. In the modified FCRM algorithm, switching prediction model is constructed considering quantification and partition of categories with the goal being to classify external factors based on task dependencies.
