# Towards the Classification of Error-Related Potentials using Riemannian Geometry

arXiv: https://arxiv.org/abs/2109.13085 · HTML: https://mlsanity.com/paper/2109.13085/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 26 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [A Plug&Play P300 BCI Using Information Geometry](https://mlsanity.com/paper/1409.0107/) (2014)
- [Liquid-crystal display (LCD) of achromatic, mean-modulated flicker in clinical assessment and experimental studies of visual systems](https://mlsanity.com/paper/2007.11151/) (2020)

## Abstract (arXiv)

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.