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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.


(A) Picture of the paradigm. The red and blue arrows indicate the movement direction of the objects (blue) and the cursor (red). The subject could move the red cursor with the gamepad to avoid a collision with one of the blue blocks, which were continuously falling down from the top of the screen. (B) Example of an outcome error, when the cursor collided with a block. (C) Example of an execution error, when the cursor moved for 2000 ms in a different direction than indicated by the subject through gamepad control. The dashed arrow in the screenshot indicates the expected movement direction, while the solid red arrow indicates the actual, erroneous direction.
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Figure 1: (A) Picture of the paradigm. The red and blue arrows indicate the movement direction of the objects (blue) and the cursor (red). The subject could move the red cursor with the gamepad to avoid a collision with one of the blue blocks, which were continuously falling down from the top of the screen. (B) Example of an outcome error, when the cursor collided with a block. (C) Example of an execution error, when the cursor moved for 2000 ms in a different direction than indicated by the subject through gamepad control. The dashed arrow in the screenshot indicates the expected movement direction, while the solid red arrow indicates the actual, erroneous direction.

Mentions: The experimental task used in this study was similar to the one described by Milekovic et al. (2012), in which the subject had to play a simple video game (depicted in Figure 1). The subject used the right thumbstick of a gamepad to control the angle in which the cursor moved on the screen. The task was to avoid collisions of the cursor with blocks dropping from the top of the screen with a constant speed. The speed of the falling blocks was set to a level that the game was challenging and the player collided with a block from time to time. In case of a collision, the game continued for 1 s and then stopped. The delay of 1 s was introduced to make sure that the reaction measured in the EEG originates from the subject recognizing the collision (outcome error) and not from the game stopping or restarting. To study the execution error, which is happening when the interface delivers erroneous feedback, the angle of the cursor movement was modified for the duration of 2 s. The degree of modification was randomized (45°, 90°, 180° to either the left or the right side). The time between two execution errors was randomized to be between 5 and 8 s.


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

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

(A) Picture of the paradigm. The red and blue arrows indicate the movement direction of the objects (blue) and the cursor (red). The subject could move the red cursor with the gamepad to avoid a collision with one of the blue blocks, which were continuously falling down from the top of the screen. (B) Example of an outcome error, when the cursor collided with a block. (C) Example of an execution error, when the cursor moved for 2000 ms in a different direction than indicated by the subject through gamepad control. The dashed arrow in the screenshot indicates the expected movement direction, while the solid red arrow indicates the actual, erroneous direction.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: (A) Picture of the paradigm. The red and blue arrows indicate the movement direction of the objects (blue) and the cursor (red). The subject could move the red cursor with the gamepad to avoid a collision with one of the blue blocks, which were continuously falling down from the top of the screen. (B) Example of an outcome error, when the cursor collided with a block. (C) Example of an execution error, when the cursor moved for 2000 ms in a different direction than indicated by the subject through gamepad control. The dashed arrow in the screenshot indicates the expected movement direction, while the solid red arrow indicates the actual, erroneous direction.
Mentions: The experimental task used in this study was similar to the one described by Milekovic et al. (2012), in which the subject had to play a simple video game (depicted in Figure 1). The subject used the right thumbstick of a gamepad to control the angle in which the cursor moved on the screen. The task was to avoid collisions of the cursor with blocks dropping from the top of the screen with a constant speed. The speed of the falling blocks was set to a level that the game was challenging and the player collided with a block from time to time. In case of a collision, the game continued for 1 s and then stopped. The delay of 1 s was introduced to make sure that the reaction measured in the EEG originates from the subject recognizing the collision (outcome error) and not from the game stopping or restarting. To study the execution error, which is happening when the interface delivers erroneous feedback, the angle of the cursor movement was modified for the duration of 2 s. The degree of modification was randomized (45°, 90°, 180° to either the left or the right side). The time between two execution errors was randomized to be between 5 and 8 s.

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.