Response error correction--a demonstration of improved human-machine performance using real-time EEG monitoring.
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.
Parra
Lucas C
LC
Sarnoff Corporation, Princeton, NJ 08540, USA. lparra@sarnoff.com
Spence
Clay D
CD
Gerson
Adam D
AD
Sajda
Paul
P
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*