Error-Related EEG Potentials Generated During Simulated Brain-Computer Interaction

Brain-computer interfaces (BCIs) are prone to errors in the recognition of subject's intent. An elegant approach to improve the accuracy of BCIs consists in a verification procedure directly based on the presence of error-related potentials (ErrP) in the electroencephalogram (EEG) recorded righ...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2008-03, Vol.55 (3), p.923-929
Main Authors: Ferrez, Pierre W., del R. Millan, JosÉ
Format: Article
Language:English
Subjects:
Algorithms
Anterior cingulate cortex (ACC)
Brain modeling
brain-computer interface (BCI)
Communication system control
Context modeling
electroencephalogram (EEG)
Electroencephalography
Electroencephalography - methods
Electronic mail
error-related potentials (ErrP)
Evoked Potentials - physiology
Feedback
Feedback - physiology
FETs
Gyrus Cinguli - physiology
Humans
Imagination - physiology
Information management
Intention
inverse models
Inverse problems
Man-Machine Systems
Motor Cortex - physiology
presupplementary motor area (pre-SMA)
Reproducibility of Results
Robotics - methods
Sensitivity and Specificity
single-trial classification
Speech recognition
User-Computer Interface
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Summary:Brain-computer interfaces (BCIs) are prone to errors in the recognition of subject's intent. An elegant approach to improve the accuracy of BCIs consists in a verification procedure directly based on the presence of error-related potentials (ErrP) in the electroencephalogram (EEG) recorded right after the occurrence of an error. Several studies show the presence of ErrP in typical choice reaction tasks. However, in the context of a BCI, the central question is: ldquoAre ErrP also elicited when the error is made by the interface during the recognition of the subject's intent?rdquo We have thus explored whether ErrP also follow a feedback indicating incorrect responses of the simulated BCI interface. Five healthy volunteer subjects participated in a new human-robot interaction experiment, which seem to confirm the previously reported presence of a new kind of ErrP. However, in order to exploit these ErrP, we need to detect them in each single trial using a short window following the feedback associated to the response of the BCI. We have achieved an average recognition rate of correct and erroneous single trials of 83.5% and 79.2%, respectively, using a classifier built with data recorded up to three months earlier.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2007.908083