Augmented cognition is an emerging concept that aims to enhance user performance and cognitive capabilities on the basis of adaptive assistance. An integral part of such systems is the automatic assessment of the instantaneous cognitive state of the user. This paper describes an automatic cognitive state estimation methodology based on the use of EEG measurements with ambulatory users. The required robustness in this context is achieved through the use of a mutual information based dimensionality reduction approach in conjunction with a committee of classifiers, and median filter outlier rejection element. We present classification results associated with cognitive tasks performed in mobile and stationary modalities.