In this chapter, we focus on analysis techniques of time series data. First, we provide a brief overview of current methods that enable imaging the human brain with high spatial and temporal resolution. Throughout the chapter we emphasize that time series analyses can be applied to different types of electrophysiological data. Second, we review analyses strategies for high-dimensional time-series data. Methods are introduced according to their practical importance during data analysis (i.e., univariate analysis approaches in the time-domain are covered first, before advancing into spectral decomposition, bivariate connectivity analyses and finally multivariate analysis strategies). We then review methods that go beyond established linear time- and/or frequency analyses and discuss non-linear approaches, including information-theoretical approaches as well as recent machine-learning inspired strategies. Finally, we take recent developments of the last five years into account, as exemplified by strategies to analyze background ‘noise’, which has recently been shown to contain important behaviorally relevant information. In addition, we highlight how analysis strategies can be synergistically combined to maximize insight into neurophysiological processes underlying human cognition. Throughout the chapter, we highlight potential caveats, with the goal to provide a roadmap for state-of-the-art electrophysiological data analysis.