Electroencephalography (EEG) is a method for measuring brain activity by recording electrical fields at the scalp surface. Although it has the highest temporal resolution among brain imaging techniques it has low spatial resolution and is very sensitive to various forms of noise (e.g. movement artifacts electrical sources in the environment impedance artifacts and various biological artifacts typically generated from muscle activation). Substantial progress in the implementation of new signal processing and statistical strategies for EEG data analysis is currently changing the specificity with which EEG researchers can interpret their data. Because EEG studies can produce large data sets (e.g. 100 participants each contributing an EEG recording that consists of 130+ recording channels for 1 hour at a common sampling rate of 500 Hz or 1000 Hz) and the new processing strategies are computationally intensive (e.g. Independen Components Analysis (ICA) and bootstrapping) the computation time involved is not feasible for many research situations. Thus often these advanced methods are not used due to computation limitations even though there is no information based downside to their outcome. In this talk I present two software extensions being developed at the Brock University Lifespan Research Center for integration with the leading open source EEG analysis software platform EEGLab (developed at the Swartz Center for Computational Neuroscience UCSD). The first is a modular interface for submitting unsupervised procedures to a compute cluster and the second is a flexible off line visualization tool that allows for the interactive annotation of extensive unsupervised processing. These software extensions together with resources such as SHARCNet can remove the computation constraints of advanced data processing from EEG research labs.