title: Optimal Multitrial Prediction Combination and Subject-Specific Adaptation for Minimal Training Brain Switch Designs.
abstract: Brain-Computer Interface (BCI) systems are traditionally designed by taking into account user-specific data to enable practical use. More recently, subject independent (SI) classification algorithms have been developed which bypass the subject specific adaptation and enable rapid use of the system. A brain switch is a particular BCI system where the system is required to distinguish from two separate mental tasks corresponding to the on-off commands of a switch. Such applications require a low false positive rate (FPR) while having an acceptable response time (RT) until the switch is activated. In this work, we develop a methodology that produces optimal brain switch behavior through subject specific (SS) adaptation of: a) a multitrial prediction combination model and b) an SI classification model. We propose a statistical model of combining classifier predictions that enables optimal FPR calibration through a short calibration session. We trained an SI classifier on a training synchronous dataset and tested our method on separate holdout synchronous and asynchronous brain switch experiments. Although our SI model obtained similar performance between training and holdout datasets, 86% and 85% for the synchronous and 69% and 66% for the asynchronous the between subject FPR and TPR variability was high (up to 62%). The short calibration session was then employed to alleviate that problem and provide decision thresholds that achieve when possible a target FPR=1% with good accuracy for both datasets.
authors.forename: Loukianos
authors.initials: L
authors.lastname: Spyrou
authors.forename: Yvonne
authors.initials: Y
authors.lastname: Blokland
authors.forename: Jason
authors.initials: J
authors.lastname: Farquhar
authors.forename: Jorgen
authors.initials: J
authors.lastname: Bruhn
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology*; D000465:Algorithms; D062207:Brain-Computer Interfaces; D003198:Computer Simulation; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013647:Task Performance and Analysis