title: Response error correction--a demonstration of improved human-machine performance using real-time EEG monitoring.
abstract: We describe a brain-computer interface (BCI) system, which uses a set of adaptive linear preprocessing and classification algorithms for single-trial detection of error related negativity (ERN). We use the detected ERN as an estimate of a subject's perceived error during an alternative forced choice visual discrimination task. The detected ERN is used to correct subject errors. Our initial results show average improvement in subject performance of 21% when errors are automatically corrected via the BCI. We are currently investigating the generalization of the overall approach to other tasks and stimulus paradigms.
authors.affiliation: Sarnoff Corporation, Princeton, NJ 08540, USA. lparra@sarnoff.com
authors.forename: Lucas C
authors.initials: LC
authors.lastname: Parra
authors.forename: Clay D
authors.initials: CD
authors.lastname: Spence
authors.forename: Adam D
authors.initials: AD
authors.lastname: Gerson
authors.forename: Paul
authors.initials: P
authors.lastname: Sajda
mesh_terms: D000465:Algorithms; D001767:Blinking / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology; D005246:Feedback; D006801:Humans; D008390:Markov Chains; D025341:Principal Component Analysis; D013269:Stochastic Processes; D014584:User-Computer Interface; D014796:Visual Perception / Q000502:physiology*