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Computer Science > Machine Learning

arXiv:2109.13085 (cs)
[Submitted on 21 Sep 2021]

Title:Towards the Classification of Error-Related Potentials using Riemannian Geometry

Authors:Yichen Tang, Jerry J. Zhang, Paul M. Corballis, Luke E. Hallum
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Abstract: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.
Comments: 4 pages, 3 figures, 1 table, submitted to and accepted by the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), this is the accepted version
Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); General Literature (cs.GL); Human-Computer Interaction (cs.HC)
ACM classes: I.5.4; J.3; J.m
Cite as: arXiv:2109.13085 [cs.LG]
  (or arXiv:2109.13085v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.13085

Submission history

From: Yichen Tang [view email]
[v1] Tue, 21 Sep 2021 06:42:47 UTC (339 KB)
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