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

5Citations
Citations of this article
24Readers
Mendeley users who have this article in their library.
Get full text

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.

Cite

CITATION STYLE

APA

Karydis, T., Foster, S. L., & Mershin, A. (2016). Self-Calibrating Protocols as diagnostic AIDS for personal medicine, neurological conditions and pain assessment. In ACM International Conference Proceeding Series (Vol. 29-June-2016). Association for Computing Machinery. https://doi.org/10.1145/2910674.2935852

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free