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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.


Left: Error-related potentials at electrode FCz for execution error (A) and outcome error (C). For display of the ErrPs, the difference between the error trials and noError trials was calculated. The colored lines depict the ErrP for the different subjects, while the bold black line is the average over all subjects. Errors are happening at t = 0 ms. The gray background denotes the time intervals with a significant difference between error and noError trials (p < 0.05, Bonferroni corrected). Right: Scalp plots showing the topographic distribution of the error-related potential for execution (B) and outcome error (D) at the time of the maximum deflection for each of the significant time intervals.
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Figure 3: Left: Error-related potentials at electrode FCz for execution error (A) and outcome error (C). For display of the ErrPs, the difference between the error trials and noError trials was calculated. The colored lines depict the ErrP for the different subjects, while the bold black line is the average over all subjects. Errors are happening at t = 0 ms. The gray background denotes the time intervals with a significant difference between error and noError trials (p < 0.05, Bonferroni corrected). Right: Scalp plots showing the topographic distribution of the error-related potential for execution (B) and outcome error (D) at the time of the maximum deflection for each of the significant time intervals.

Mentions: The average event-related potentials for NoError trials, execution errors, and outcome errors are shown in Figure 2, along with the significant differences between execution and outcome error. Figure 3 shows the average difference waveform of execution error and outcome error at electrode FCz for all subjects, as well as the topographic distribution of the potential. It can be seen that a clear potential is visible for both kinds of error. The topographic distribution is similar for both errors and all subjects, with the maximum around electrode FCz and Cz. However, the waveform shape of the two error potentials differs strongly. For the execution error, we found a positive peak at 229 ms, a negative peak at 287 ms, a positive peak at 367 ms, and a small negative peak at 461 ms. In contrast, the outcome ErrP starts with a negative peak at 2 ms, followed by a positive peak at 268 ms, a negative peak at 486 ms, and a small positivity at 742 ms.


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

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

Left: Error-related potentials at electrode FCz for execution error (A) and outcome error (C). For display of the ErrPs, the difference between the error trials and noError trials was calculated. The colored lines depict the ErrP for the different subjects, while the bold black line is the average over all subjects. Errors are happening at t = 0 ms. The gray background denotes the time intervals with a significant difference between error and noError trials (p < 0.05, Bonferroni corrected). Right: Scalp plots showing the topographic distribution of the error-related potential for execution (B) and outcome error (D) at the time of the maximum deflection for each of the significant time intervals.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Left: Error-related potentials at electrode FCz for execution error (A) and outcome error (C). For display of the ErrPs, the difference between the error trials and noError trials was calculated. The colored lines depict the ErrP for the different subjects, while the bold black line is the average over all subjects. Errors are happening at t = 0 ms. The gray background denotes the time intervals with a significant difference between error and noError trials (p < 0.05, Bonferroni corrected). Right: Scalp plots showing the topographic distribution of the error-related potential for execution (B) and outcome error (D) at the time of the maximum deflection for each of the significant time intervals.
Mentions: The average event-related potentials for NoError trials, execution errors, and outcome errors are shown in Figure 2, along with the significant differences between execution and outcome error. Figure 3 shows the average difference waveform of execution error and outcome error at electrode FCz for all subjects, as well as the topographic distribution of the potential. It can be seen that a clear potential is visible for both kinds of error. The topographic distribution is similar for both errors and all subjects, with the maximum around electrode FCz and Cz. However, the waveform shape of the two error potentials differs strongly. For the execution error, we found a positive peak at 229 ms, a negative peak at 287 ms, a positive peak at 367 ms, and a small negative peak at 461 ms. In contrast, the outcome ErrP starts with a negative peak at 2 ms, followed by a positive peak at 268 ms, a negative peak at 486 ms, and a small positivity at 742 ms.

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.