Performance characterization of self-calibrating protocols for wearable EEG applications

Abstract

Therapeutic Neurofeedback (NFB) using real-time electroencephalography (EEG) data works by reinforcing desired brainwave patterns. Although EEG is a well-established diagnostic tool and EEG-NFB shows great promise for enhancing cognitive performance and treating neurological disorders, proof of its efficacy has been limited. Here we characterize a novel Self-Calibrating Protocol (SCP) method coupled to five standard machine learning algorithms to classify brain states corresponding to the experience of "pain" or "no pain".