The functional Magnetic Resonance Imaging (fMRI) is one of the most important tools for exploring the operation of the brain as it allows the spatially localized characteristics of brain activity to be observed. However, fMRI studies generate huge volumes of data and the signals of interest have low signal to noise ratio making its analysis a very challenging problem. There is a growing need for new methods that can efficiently and objectively extract the useful information from fMRI data and translate it into intelligible knowledge. In this paper, we introduce a swarm-based rough set approach to fMRI data analysis. Our approach is based on exploiting the power of particle swarm optimization to discover the feature combinations in an efficient manner by observing the change in positive region as the particles proceed through the search space. The approach supports multi-knowledge extraction. We evaluate the performance of the algorithm using benchmark and fMRI datasets. The results demonstrate its potential value for cognition research.