Self-Calibrating Protocols as diagnostic AIDS for personal medicine, neurological conditions and pain assessment

Abstract

Recent advances in the tracking and quantification of pain using consumer-grade wearable EEG headbands, such as Muse [8] and Neurosky [12], coupled to effcient machine learning [9], pave the way towards applying Self-Calibrating Protocols (SCP) [10] and Dynamic Background Reduction (DBR) [11] principles to basic research, while empowering new applications. In the cases of neurological conditions and chronic pain management, SCP is of particular inter- est during the early diagnostic process as well as an aid in personalizing intervention strategies. In this paper, we out- line a framework based on SCP, to design machine learning systems that completely bypass the pitfalls of using normed neurophysiological states for diagnostics. This effort targets short-term practical development of personalized early di- agnostics and treatment strategies and has longer-term im- plications for Brain-Computer Interface (BCI) and Human- Computer Interaction (HCI) methodologies.