Abstract
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.
Highlights
If a person makes or perceives an error, an error-related potential can be detected in the electroencephalogram (EEG) due to the person recognizing that error (Falkenstein et al, 2000)
As the error-related potential (ErrP) in Brain-Computer Interfaces (BCIs) applications consists of multiple components, ErrP is the commonly used term (Chavarriaga et al, 2014) in the BCI literature and generally considered as an umbrella term, which comprises all components of the event-related potential that can be measured in response to an error
Based on the results by Milekovic et al, the study presented in this paper aims at answering three questions: (i) Can ErrPs be found in EEG during a cursor control task with continuous feedback? (ii) Can machine learning methods be used to detect and discriminate execution and outcome errors in EEG? (iii) Can the severity of an error be detected in EEG?
Summary
If a person makes or perceives an error, an error-related potential can be detected in the electroencephalogram (EEG) due to the person recognizing that error (Falkenstein et al, 2000). ErrPs have mainly been utilized in BCIs with discrete feedback, which is why we want to investigate the detection. Since the interest of this study is not in one of the components of the ErrP or the neurophysiological interpretation, but the investigation of the error-related response in general with regards to its utilization in continuous BCI systems, we use the term error-related potential (ErrP). As the ErrP in BCI applications consists of multiple components, ErrP is the commonly used term (Chavarriaga et al, 2014) in the BCI literature and generally considered as an umbrella term, which comprises all components of the event-related potential that can be measured in response to an error
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