Response error correction--a demonstration of improved human-machine performance using real-time EEG monitoring.

Lucas C Parra; Clay D Spence; Adam D Gerson; Paul Sajda

doi:10.1109/tnsre.2003.814446

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Response error correction--a demonstration of improved human-machine performance using real-time EEG monitoring.

Lucas C Parra·Clay D Spence·Adam D Gerson·Paul Sajda

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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.

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Algorithms Blinking Brain Discriminant Analysis Electroencephalography Evoked Potentials Feedback Humans Markov Chains Principal Component Analysis Stochastic Processes User-Computer Interface Visual Perception Comparative Study Evaluation Study Journal Article Research Support, U.S. Gov't, Non-P.H.S.Validation Study

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Lucas C Parra, Clay D Spence, Adam D Gerson, Paul Sajda. 2003. Response error correction--a demonstration of improved human-machine performance using real-time EEG monitoring.. https://doi.org/10.1109/tnsre.2003.814446

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