  Reinforcement learning (RL) and brain-computer interfaces (BCI) have
experienced significant growth over the past decade. With rising interest in
human-in-the-loop (HITL), incorporating human input with RL algorithms has
given rise to the sub-field of interactive RL. Adjacently, the field of BCI has
long been interested in extracting informative brain signals from neural
activity for use in human-computer interactions. A key link between these
fields lies in the interpretation of neural activity as feedback such that
interactive RL approaches can be employed. We denote this new and emerging
medium of feedback as intrinsic feedback. Despite intrinsic feedback's ability
to be conveyed automatically and even unconsciously, proper exploration
surrounding this key link has largely gone unaddressed by both communities.
Thus, to help facilitate a deeper understanding and a more effective
utilization, we provide a tutorial-style review covering the motivations,
approaches, and open problems of intrinsic feedback and its foundational
concepts.
