Functional feature subspace mapping of fMRI data in the spectral domain

Abstract

We propose a new method for the analysis of functional magnetic resonance imaging (fMRI) which is called functional feature subspace mapping (FFSM). We mainly focused on the experimental design with periodic stimuli which can be described by a number of Fourier coefficients in the spectral domain. Then the subspace is obtained through the dimension reduction technique. Finally, the presence of activated time series is identified by the clustering method. Experiments with simulated data and the real human experiments are conducted to demonstrate that the algorithm we proposed is feasible. Although we focus on analyzing periodic fMRI data, the approach could be extended to analyze non-periodic fMRI data (event-related fMRI) by replacing the spectral analysis with a wavelet analysis.