Yes. You can apply the GSS analysis method to any dataset (as long as you have multiple functional runs per subject; multiple runs are important for cross-validation).
For example, you can use the GSS analysis as a powerful alternative to the traditional random-effects analysis. In such case, you would use your main contrast as both the "localizer" contrast and the "effect of interest" contrast. The toolbox we developed would perform cross-validation in such case, as described here, so that the effect of interest is estimated using data not used for defining subject-specific fROIs. We discuss this procedure in more detail in Nieto-Castañon & Fedorenko (2012).
Alternatively, if you have multiple conditions in your study, you may choose to use some conditions for defining subject-specific fROIs and examine the response to the other conditions.
If you don’t have much data per subject, it is not unreasonable to work with liberal thresholds: given that all the effects are always estimated in a left-out portion of the data, even regions discovered at liberal thresholds in individual subjects can be thought of as meaningful regions if they show replicable response profiles.