  Identifying stimulus-driven neural activity patterns is critical for studying
the neural basis of cognition. This can be particularly challenging in
intracranial datasets, where electrode locations typically vary across
patients. This chapter first presents an overview of the major challenges to
identifying stimulus-driven neural activity patterns in the general case. Next,
we will review several modality-specific considerations and approaches, along
with a discussion of several issues that are particular to intracranial
recordings. Against this backdrop, we will consider a variety of within-subject
and across-subject approaches to identifying and modeling stimulus-driven
neural activity patterns in multi-patient intracranial recordings. These
approaches include generalized linear models, multivariate pattern analysis,
representational similarity analysis, joint stimulus-activity models,
hierarchical matrix factorization models, Gaussian process models, geometric
alignment models, inter-subject correlations, and inter-subject functional
correlations. Examples from the recent literature serve to illustrate the major
concepts and provide the conceptual intuitions for each approach.
