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Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity

When a person recognizes an error during a task, an error-related potential (ErrP) can be measured as response. It has been shown that ErrPs can be automatically detected in tasks with time-discrete feedback, which is widely applied in the field of Brain-Computer Interfaces (BCIs) for error correcti...

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Autores principales: Spüler, Martin, Niethammer, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4374466/
https://www.ncbi.nlm.nih.gov/pubmed/25859204
http://dx.doi.org/10.3389/fnhum.2015.00155
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author Spüler, Martin
Niethammer, Christian
author_facet Spüler, Martin
Niethammer, Christian
author_sort Spüler, Martin
collection PubMed
description When a person recognizes an error during a task, an error-related potential (ErrP) can be measured as response. It has been shown that ErrPs can be automatically detected in tasks with time-discrete feedback, which is widely applied in the field of Brain-Computer Interfaces (BCIs) for error correction or adaptation. However, there are only a few studies that concentrate on ErrPs during continuous feedback. With this study, we wanted to answer three different questions: (i) Can ErrPs be measured in electroencephalography (EEG) recordings during a task with continuous cursor control? (ii) Can ErrPs be classified using machine learning methods and is it possible to discriminate errors of different origins? (iii) Can we use EEG to detect the severity of an error? To answer these questions, we recorded EEG data from 10 subjects during a video game task and investigated two different types of error (execution error, due to inaccurate feedback; outcome error, due to not achieving the goal of an action). We analyzed the recorded data to show that during the same task, different kinds of error produce different ErrP waveforms and have a different spectral response. This allows us to detect and discriminate errors of different origin in an event-locked manner. By utilizing the error-related spectral response, we show that also a continuous, asynchronous detection of errors is possible. Although the detection of error severity based on EEG was one goal of this study, we did not find any significant influence of the severity on the EEG.
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spelling pubmed-43744662015-04-09 Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity Spüler, Martin Niethammer, Christian Front Hum Neurosci Neuroscience When a person recognizes an error during a task, an error-related potential (ErrP) can be measured as response. It has been shown that ErrPs can be automatically detected in tasks with time-discrete feedback, which is widely applied in the field of Brain-Computer Interfaces (BCIs) for error correction or adaptation. However, there are only a few studies that concentrate on ErrPs during continuous feedback. With this study, we wanted to answer three different questions: (i) Can ErrPs be measured in electroencephalography (EEG) recordings during a task with continuous cursor control? (ii) Can ErrPs be classified using machine learning methods and is it possible to discriminate errors of different origins? (iii) Can we use EEG to detect the severity of an error? To answer these questions, we recorded EEG data from 10 subjects during a video game task and investigated two different types of error (execution error, due to inaccurate feedback; outcome error, due to not achieving the goal of an action). We analyzed the recorded data to show that during the same task, different kinds of error produce different ErrP waveforms and have a different spectral response. This allows us to detect and discriminate errors of different origin in an event-locked manner. By utilizing the error-related spectral response, we show that also a continuous, asynchronous detection of errors is possible. Although the detection of error severity based on EEG was one goal of this study, we did not find any significant influence of the severity on the EEG. Frontiers Media S.A. 2015-03-26 /pmc/articles/PMC4374466/ /pubmed/25859204 http://dx.doi.org/10.3389/fnhum.2015.00155 Text en Copyright © 2015 Spüler and Niethammer. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Spüler, Martin
Niethammer, Christian
Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity
title Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity
title_full Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity
title_fullStr Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity
title_full_unstemmed Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity
title_short Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity
title_sort error-related potentials during continuous feedback: using eeg to detect errors of different type and severity
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4374466/
https://www.ncbi.nlm.nih.gov/pubmed/25859204
http://dx.doi.org/10.3389/fnhum.2015.00155
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