What are good single-trial experiments to test out dry sensors? Ideally, the experiments should be generally accepted, i.e., published in a peer-reviewed journal. The idea would be to repeat the experiment of interest comparing performance for dry versus wet electrodes using the published method of analysis. A single-trial scenario is important because we want to be able to claim that it isn't that noise from the sensors is not being averaged away. These sorts of experiments can come in two flavors (sort of). They can be focused on the so-called Brain Computer Interface (BCI) or on classification of some sort. In reality, BCI is a subset of classification experiments, where the practicality of entering information into a system has been considered. These mostly have to do with recognizing the readiness potential prior to hand or finger movement. An example of a non-BCI single-trial classification exercise would be the single-trial classification of stimuli such as words or sentences. A search on PubMed on "single trial EEG" brought up 277 references, none of which were available online. IEEE Xplore offers full-text articles, so I went there next, seraching for the same phrase.
Bereitschaftspotential (BP) is German for "readiness potential". It is a negative potential detectable via EEG occuring several hundred milliseconds before movement. As I mentioned earlier, a group at Fraunhofer-FIRST has been working on the classification of BP in single-trial recordings. In , Blankertz et al. demonstrated both response-aligned classification (~97%) and a pseudo-online classification using two classifiers. In this discussion, I will focus on their response-aligned classification which was essentially a two class problem (left hand versus right).
 B. Blankertz, G. Curio and K. Müller, "Classifying Single Trial EEG: Towards Brain Computer Interfacing," in Advances in Neural Information Processing Systems, vol. 14, T.G. Dietterich, S. Becker and Z. Ghahramani Eds. Cambridge, MA: MIT Press, 2002, pp. 157-164.