Performing Independent Component Analysis (ICA) on biomedical signals is quite commonplace. ICA is usually applied to multi-channel data however not always with great success. In previous work we realized an innovation to standard ICA which we call space-time ICA (ST-ICA). This method brings into play both spatial and temporal/spectral information to perform very powerful extractions and overcomes the individual limitations of ensemble (spatial) ICA and single-channel (temporal) ICA. The cost in implementing ST-ICA is the curse of dimensionality since spatio-temporal analysis of multi-channel physiological data recorded at suitable sampling speeds results in large unwieldy datasets which become impossible to parse without any form of truncation or at least an automated component selection process. Here we address the component selection problem on the application of ST -ICA to real-world neurophysiological data-specifically in extracting seizure data from EEG recordings. We assess the information held in each of the spatio-temporal features resulting from ST-ICA and comment on the development of an efficient method to extract them, as well as using dimensional reduction techniques to reduce the curse of dimensionality resulting successful separation of meaningful physiological data from noisy, artifact laden datasets. Clinical Relevance-These methods will allow for the automatic identification and extraction of poorly defined episodes of physiologically meaningful activity in noisy multi-channel recordings of brain signals.