Significant growth in the field of neuroscience has occurred over the last decade such that new application areas for basic research techniques are opening up to practitioners in many other areas. Of particular interest to many is the principle of neuroergonomics, by which the traditional work in neuroscience and its related topics can be applied to non-traditional areas such as human-machine system design. While work in neuroergonomics certainly predates the use of the term in the literature (previously identified by others as applied neuroscience, operational neuroscience, etc.), there is great promise in the larger framework that is represented by the general context of the terminology. Here, we focus on the very specific concept that principles in brain-computer interfaces, neural prosthetics and the larger realm of machine learning using physiological inputs can be applied directly to the design and implementation of augmented human-machine systems. Indeed, work in this area has been ongoing for more than 25 years with very little cross-talk and collaboration between clinical and applied researchers. We propose that, given increased interest in augmented human-machine systems based on cognitive state, further progress will require research in the same vein as that being done in the aforementioned communities, and that all researchers with a vested interest in physiologically-based machine learning techniques can benefit from increased collaboration. We thereby seek to describe the current state of cognitive state assessment in human-machine systems, the problems and challenges faced, and the tightly-coupled relationship with other research areas. This supports the larger work of the Cognitive State Assessment 2011 Competition by setting the stage for the purpose of the session by showing the need to increase research in the machine learning techniques used by practitioners of augmented human-machine system design.