Computer processing of biomedical signals have gained a lot of importance because its extensive applications in healthcare. Extensive studies have been made on various aspects of bioengineering and in this paper we consider its application to neuroscience One of the important area of study is on the  functional connectivity study since it provides a new way of measuring the interdependency of brain regions. This has been widely used to study the neural basis of processes such as perception, cognition, and emotion. In this paper, we have studied and compared functional connectivity of two altered states of consciousness, pre-meditation and meditation, in several practitioners of Raja Yoga meditation. For quantifying functional relationships between EEG channels, we have used mutual information measure. The pair-wise dependence measures between the EEG channels form connection matrix of the graph, and various graph parameters, such as connection density, degree of nodes, characteristic path length, and cluster index, is computed in each of the experimental cases.  Statistical significance of the parameters is tested between the states using Kruskal-Wallis test. The graph theoretic parameters can discriminate between meditation state and pre-meditation state in theta and beta bands of EEG. The functional connectivity during meditation is frequency specific and synchronization is uniformly spread over scalp. The connectivity graph during meditation has high cluster coefficient and short characteristic path length. This property suggests small-world nature of the graph during meditation. Thus the paper has demonstrated the use computer based techniques in studying altered states of consciousness which in turn is  useful in gaining fundamental understanding of functioning of the brain.

: Electroencephalogram,Computer processing, Brain functional connectivity, Mutual information, Graph theory