RESEARCH ARTICLE


Classification of Event-Related Potentials Associated with Response Errors in Actors and Observers Based on Autoregressive Modeling



Christos E Vasios1, Errikos M Ventouras*, 2, George K Matsopoulos1, Irene Karanasiou1, Pantelis Asvestas2, Nikolaos K Uzunoglu1, Hein T Van Schie3, Ellen R.A de Bruijn3
1 Institute of Communications and Computer Systems, National Technical University of Athens, Athens, Greece
2 Department of Medical Instrumentation Technology, Technological Educational Institution of Athens, Athens, Greece
3 Nijmegen Institute for Cognition and Information, University of Nijmegen, Nijmegen, The Netherlands


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© Vasios et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Department of Medical Instrumentation Technology, Technological Educational Institution of Athens, Ag. Spyridonos Str., Egaleo, Athens, 12210, Greece; Tel: ++302105385387; Fax: ++302105385302; E-mails: ericvent@teiath.gr, ericvent@gmail.com


Abstract

Event-Related Potentials (ERPs) provide non-invasive measurements of the electrical activity on the scalp related to the processing of stimuli and preparation of responses by the brain. In this paper an ERP-signal classification method is proposed for discriminating between ERPs of correct and incorrect responses of actors and of observers seeing an actor making such responses. The classification method targeted signals containing error-related negativity (ERN) and error positivity (Pe) components, which are typically associated with error processing in the human brain. Feature extraction consisted of Multivariate Autoregressive modeling combined with the Simulated Annealing technique. The resulting information was subsequently classified by means of an Artificial Neural Network (ANN) using back-propagation algorithm under the “leave-one-out cross-validation” scenario and the Fuzzy C-Means (FCM) algorithm. The ANN consisted of a multi-layer perceptron (MLP). The approach yielded classification rates of up to 85%, both for the actors’ correct and incorrect responses and the corresponding ERPs of the observers. The electrodes needed for such classifications were situated mainly at central and frontal areas. Results provide indications that the classification of the ERN is achievable. Furthermore, the availability of the Pe signals, in addition to the ERN, improves the classification, and this is more pronounced for observers’ signals. The proposed ERP-signal classification method provides a promising tool to study error detection and observational-learning mechanisms in performance monitoring and joint-action research, in both healthy and patient populations.