Ever since the first discovery of human brain waves in 1929, brain rhythm has been attracting interest in the field of neuroscience. The integration of distributed brain functions similar to small-scale circuits for the same task in a larger scale network which oscillations facilitate offers a means to study the brain at work. Importantly, changes in synchronized brain oscillations may reveal important aspects of pathophysiology. For example, excess beta rhythms are characteristic of Parkinson's brain. However, various spatial distributions and frequencies of neuronal oscillations and nonlinear and complicated neuronal processes make it difficult to understand neuronal messages, and it is needed to find an appropriate model. Thus, we present a brief review of techniques used in characterizing frequency-related local fluctuations and interactions between neuronal assemblies by measuring electroencephalography (EEG) or MEG. Specifically, we focus on the objectives of these methods, including: (1) inferential versus non-inferential, (2) linear versus nonlinear, (3) uni-versus multi-variate, and (4) power modulation versus phase-synchrony. Three practical issues – that are typically confronted when applying these methods – are also discussed. This article aims to provide readers who are not familiar with current methods an accessible overview – that may help the neuroscientists to interpret the similar findings of this study.
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