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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.


AUC for the asynchronous classification separated by execution error and outcome error. Sensitivity (true positive rate) and specificity (true negative rate) were calculated based on the continuous classification in 62.5 ms steps. Each red line represents the data of one subject. The dashed line represents chance level. Results are significantly above chance level for all subjects (p < 0.05).
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Figure 5: AUC for the asynchronous classification separated by execution error and outcome error. Sensitivity (true positive rate) and specificity (true negative rate) were calculated based on the continuous classification in 62.5 ms steps. Each red line represents the data of one subject. The dashed line represents chance level. Results are significantly above chance level for all subjects (p < 0.05).

Mentions: The AUC for the asynchronous classification of the two errors is shown in Figure 5. On average, the AUC for execution error is 0.692, while for the outcome error we obtained an average AUC of 0.657. More detailed results for all subjects can be found in Table 2. The AUC is significantly above chance level for all subjects (p < 0.05, permutation test).


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

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

AUC for the asynchronous classification separated by execution error and outcome error. Sensitivity (true positive rate) and specificity (true negative rate) were calculated based on the continuous classification in 62.5 ms steps. Each red line represents the data of one subject. The dashed line represents chance level. Results are significantly above chance level for all subjects (p < 0.05).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: AUC for the asynchronous classification separated by execution error and outcome error. Sensitivity (true positive rate) and specificity (true negative rate) were calculated based on the continuous classification in 62.5 ms steps. Each red line represents the data of one subject. The dashed line represents chance level. Results are significantly above chance level for all subjects (p < 0.05).
Mentions: The AUC for the asynchronous classification of the two errors is shown in Figure 5. On average, the AUC for execution error is 0.692, while for the outcome error we obtained an average AUC of 0.657. More detailed results for all subjects can be found in Table 2. The AUC is significantly above chance level for all subjects (p < 0.05, permutation test).

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.