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With increased attention toward physiological cognitive state assessment as a component in the larger field of applied neuroscience, the need to develop methods for robust, stable assessment of cognitive state has been expressed as critical to designing effective augmented human-machine systems. The technique of cognitive state assessment, as well as its benefits, has been demonstrated by many research groups. In an effort to move closer toward a realized system, efforts must now be focused on critical issues that remain unsolved, namely instability of pattern classifiers over the course of hours and days. This work, as part of the Cognitive State Assessment Competition 2011, seeks to explore methods for `learning' non-stationarity as a mitigation for more generalized patterns that are stable over time courses that are not widely discussed in the literature.