  The error-related potential (ErrP) is an event-related potential (ERP) evoked
by an experimental participant's recognition of an error during task
performance. ErrPs, originally described by cognitive psychologists, have been
adopted for use in brain-computer interfaces (BCIs) for the detection and
correction of errors, and the online refinement of decoding algorithms.
Riemannian geometry-based feature extraction and classification is a new
approach to BCI which shows good performance in a range of experimental
paradigms, but has yet to be applied to the classification of ErrPs. Here, we
describe an experiment that elicited ErrPs in seven normal participants
performing a visual discrimination task. Audio feedback was provided on each
trial. We used multi-channel electroencephalogram (EEG) recordings to classify
ErrPs (success/failure), comparing a Riemannian geometry-based method to a
traditional approach that computes time-point features. Overall, the Riemannian
approach outperformed the traditional approach (78.2% versus 75.9% accuracy, p
< 0.05); this difference was statistically significant (p < 0.05) in three of
seven participants. These results indicate that the Riemannian approach better
captured the features from feedback-elicited ErrPs, and may have application in
BCI for error detection and correction.
