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

Spüler M, Niethammer C - Front Hum Neurosci (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Computer Science Department, University of Tübingen Tübingen, Germany.

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.

No MeSH data available.


R2 values showing the difference in power for different frequency bands between noError trials and execution error (A) or outcome error (B), respectively.
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Figure 4: R2 values showing the difference in power for different frequency bands between noError trials and execution error (A) or outcome error (B), respectively.

Mentions: Regarding the frequency spectra of the observed error potentials (see Figure 4), we found activity mainly in the delta (1–4 Hz) and theta (5–7 Hz) frequency band for both errors, but the errors show a different spatial power distribution. For the execution error, the activity in both bands is strictly located at electrode Cz. For outcome errors, activity in the delta band can be seen mainly around Cz, while Fz and FCz show activity in the theta band.


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

Spüler M, Niethammer C - Front Hum Neurosci (2015)

R2 values showing the difference in power for different frequency bands between noError trials and execution error (A) or outcome error (B), respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4374466&req=5

Figure 4: R2 values showing the difference in power for different frequency bands between noError trials and execution error (A) or outcome error (B), respectively.
Mentions: Regarding the frequency spectra of the observed error potentials (see Figure 4), we found activity mainly in the delta (1–4 Hz) and theta (5–7 Hz) frequency band for both errors, but the errors show a different spatial power distribution. For the execution error, the activity in both bands is strictly located at electrode Cz. For outcome errors, activity in the delta band can be seen mainly around Cz, while Fz and FCz show activity in the theta band.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Computer Science Department, University of Tübingen Tübingen, Germany.

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.

No MeSH data available.