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Invariance and variability in interaction error-related potentials and their consequences for classification

Abu-Alqumsan, Mohammad; Kapeller, Christoph; Hinterm�ller, Christoph; Guger, Christoph; Peer, Angelika

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Authors

Mohammad Abu-Alqumsan

Christoph Kapeller

Christoph Hinterm�ller

Christoph Guger



Abstract

© 2017 IOP Publishing Ltd. Objective. This paper discusses the invariance and variability in interaction error-related potentials (ErrPs), where a special focus is laid upon the factors of (1) the human mental processing required to assess interface actions (2) time (3) subjects. Approach. Three different experiments were designed as to vary primarily with respect to the mental processes that are necessary to assess whether an interface error has occurred or not. The three experiments were carried out with 11 subjects in a repeated-measures experimental design. To study the effect of time, a subset of the recruited subjects additionally performed the same experiments on different days. Main results. The ErrP variability across the different experiments for the same subjects was found largely attributable to the different mental processing required to assess interface actions. Nonetheless, we found that interaction ErrPs are empirically invariant over time (for the same subject and same interface) and to a lesser extent across subjects (for the same interface). Significance. The obtained results may be used to explain across-study variability of ErrPs, as well as to define guidelines for approaches to the ErrP classifier transferability problem.

Citation

Abu-Alqumsan, M., Kapeller, C., Hintermüller, C., Guger, C., & Peer, A. (2017). Invariance and variability in interaction error-related potentials and their consequences for classification. Journal of Neural Engineering, 14(6), 066015. https://doi.org/10.1088/1741-2552/aa8416

Journal Article Type Article
Acceptance Date Aug 4, 2017
Online Publication Date Nov 23, 2017
Publication Date Nov 23, 2017
Deposit Date Aug 7, 2017
Publicly Available Date Mar 28, 2024
Journal Journal of Neural Engineering
Print ISSN 1741-2560
Electronic ISSN 1741-2552
Publisher IOP Publishing
Peer Reviewed Peer Reviewed
Volume 14
Issue 6
Pages 066015
DOI https://doi.org/10.1088/1741-2552/aa8416
Keywords EEG, BCI, error-related potentials, P300, invariance, classification
Public URL https://uwe-repository.worktribe.com/output/877652
Publisher URL https://doi.org/10.1088/1741-2552/aa8416
Additional Information Additional Information : This is the author's accepted manuscript. The final published version is available at https://doi.org/10.1088/1741-2552/aa8416

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