  One approach, for understanding human brain functioning, is to analyze the
changes in the brain while performing cognitive tasks. Towards this, Functional
Magnetic Resonance (fMR) images of subjects performing well-defined tasks are
widely utilized for task-specific analyses. In this work, we propose a
procedure to enable classification between two chosen cognitive tasks, using
their respective fMR image sequences. The time series of expert-marked
anatomically-mapped relevant voxels are processed and fed as input to the
classical Naive Bayesian and SVM classifiers. The processing involves use of
random sieve function, phase information in the data transformed using Fourier
and Hilbert transformations. This processing results in improved
classification, as against using the voxel intensities directly, as
illustrated. The novelty of the proposed method lies in utilizing the phase
information in the transformed domain, for classifying between the cognitive
tasks along with random sieve function chosen with a particular probability
distribution. The proposed classification procedure is applied on a publicly
available dataset, StarPlus data, with 6 subjects performing the two distinct
cognitive tasks of watching either a picture or a sentence. The classification
accuracy stands at an average of 65.6%(using Naive Bayes classifier) and
76.4%(using SVM classifier) for raw data. The corresponding classification
accuracy stands at 96.8% and 97.5% for Fourier transformed data. For Hilbert
transformed data, it is 93.7% and 99%, for 6 subjects, on 2 cognitive tasks.
