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

• Published in: IEEE transactions on biomedical engineering Vol. 55; no. 3; pp. 923 - 929
• Main Authors: Ferrez, Pierre W., del R. Millan, JosÉ
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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; Electroen-cephalogram (EEG); Brain-computer interface (BCI); Inverse models; Error-related potentials (ErrP); Anterior cingulate cortex (ACC); Single-trial classification
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2007.908083

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.