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Estimating endogenous changes in task performance from EEG.

Touryan J, Apker G, Lance BJ, Kerick SE, Ries AJ, McDowell K - Front Neurosci (2014)

Bottom Line: For the perceptual discrimination task we generated estimates of accuracy, reaction time, and button press duration for each participant.The correlation coefficients between the actual and estimated behavior were similar for these three metrics (accuracy = 0.25 ± 0.37, reaction time = 0.33 ± 0.23, button press duration = 0.36 ± 0.30).These findings illustrate the potential for modeling time-on-task decrements in performance from concurrent measures of neural activity.

View Article: PubMed Central - PubMed

Affiliation: U.S. Army Research Laboratory, Human Research and Engineering Directorate Aberdeen Proving Ground, MD, USA.

ABSTRACT
Brain wave activity is known to correlate with decrements in behavioral performance as individuals enter states of fatigue, boredom, or low alertness.Many BCI technologies are adversely affected by these changes in user state, limiting their application and constraining their use to relatively short temporal epochs where behavioral performance is likely to be stable. Incorporating a passive BCI that detects when the user is performing poorly at a primary task, and adapts accordingly may prove to increase overall user performance. Here, we explore the potential for extending an established method to generate continuous estimates of behavioral performance from ongoing neural activity; evaluating the extended method by applying it to the original task domain, simulated driving; and generalizing the method by applying it to a BCI-relevant perceptual discrimination task. Specifically, we used EEG log power spectra and sequential forward floating selection (SFFS) to estimate endogenous changes in behavior in both a simulated driving task and a perceptual discrimination task. For the driving task the average correlation coefficient between the actual and estimated lane deviation was 0.37 ± 0.22 (μ ± σ). For the perceptual discrimination task we generated estimates of accuracy, reaction time, and button press duration for each participant. The correlation coefficients between the actual and estimated behavior were similar for these three metrics (accuracy = 0.25 ± 0.37, reaction time = 0.33 ± 0.23, button press duration = 0.36 ± 0.30). These findings illustrate the potential for modeling time-on-task decrements in performance from concurrent measures of neural activity.

No MeSH data available.


Related in: MedlinePlus

Topological and spectral distribution of the adaptive model in the driving and RSVP tasks. (A) Grand average channel montage (left) and spectrum of relative weights (right) for the optimal model in the driving task. Size of circle indicates relative frequency of inclusion across participants. Black shading indicates spectral weights with values significantly different than zero (p < 0.05 with FDR correction). (B–D) Channel montage and spectrum for the three behavioral metrics in the RSVP task.
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Figure 9: Topological and spectral distribution of the adaptive model in the driving and RSVP tasks. (A) Grand average channel montage (left) and spectrum of relative weights (right) for the optimal model in the driving task. Size of circle indicates relative frequency of inclusion across participants. Black shading indicates spectral weights with values significantly different than zero (p < 0.05 with FDR correction). (B–D) Channel montage and spectrum for the three behavioral metrics in the RSVP task.

Mentions: Figure 9 shows the average topological distribution of included channels in the adaptive modeling scheme for both the driving and RSVP tasks. To quantify the gross features of this topology, we used the following approach. First, for each participant we normalized the channel distribution by the total number of channels included in their optimal model (between 1 and 12). We then separated the normalized distribution by hemisphere in two ways: anterior-posterior and left-right. For the first comparison we utilized the driving and RSVP-accuracy distributions. We performed an ANOVA with two factors (task × location) but did not identify any significant topological effects between tasks. We then performed an additional two factor ANOVA (metric × location) for the distributions within the RSVP task. While there was no significant clustering of channels across all metrics, there was a significant interaction between metric and left-right distribution [F(2, 24) = 3.497, p < 0.05]. Here, the accuracy and duration models tended to select more channels from the right hemisphere.


Estimating endogenous changes in task performance from EEG.

Touryan J, Apker G, Lance BJ, Kerick SE, Ries AJ, McDowell K - Front Neurosci (2014)

Topological and spectral distribution of the adaptive model in the driving and RSVP tasks. (A) Grand average channel montage (left) and spectrum of relative weights (right) for the optimal model in the driving task. Size of circle indicates relative frequency of inclusion across participants. Black shading indicates spectral weights with values significantly different than zero (p < 0.05 with FDR correction). (B–D) Channel montage and spectrum for the three behavioral metrics in the RSVP task.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4061490&req=5

Figure 9: Topological and spectral distribution of the adaptive model in the driving and RSVP tasks. (A) Grand average channel montage (left) and spectrum of relative weights (right) for the optimal model in the driving task. Size of circle indicates relative frequency of inclusion across participants. Black shading indicates spectral weights with values significantly different than zero (p < 0.05 with FDR correction). (B–D) Channel montage and spectrum for the three behavioral metrics in the RSVP task.
Mentions: Figure 9 shows the average topological distribution of included channels in the adaptive modeling scheme for both the driving and RSVP tasks. To quantify the gross features of this topology, we used the following approach. First, for each participant we normalized the channel distribution by the total number of channels included in their optimal model (between 1 and 12). We then separated the normalized distribution by hemisphere in two ways: anterior-posterior and left-right. For the first comparison we utilized the driving and RSVP-accuracy distributions. We performed an ANOVA with two factors (task × location) but did not identify any significant topological effects between tasks. We then performed an additional two factor ANOVA (metric × location) for the distributions within the RSVP task. While there was no significant clustering of channels across all metrics, there was a significant interaction between metric and left-right distribution [F(2, 24) = 3.497, p < 0.05]. Here, the accuracy and duration models tended to select more channels from the right hemisphere.

Bottom Line: For the perceptual discrimination task we generated estimates of accuracy, reaction time, and button press duration for each participant.The correlation coefficients between the actual and estimated behavior were similar for these three metrics (accuracy = 0.25 ± 0.37, reaction time = 0.33 ± 0.23, button press duration = 0.36 ± 0.30).These findings illustrate the potential for modeling time-on-task decrements in performance from concurrent measures of neural activity.

View Article: PubMed Central - PubMed

Affiliation: U.S. Army Research Laboratory, Human Research and Engineering Directorate Aberdeen Proving Ground, MD, USA.

ABSTRACT
Brain wave activity is known to correlate with decrements in behavioral performance as individuals enter states of fatigue, boredom, or low alertness.Many BCI technologies are adversely affected by these changes in user state, limiting their application and constraining their use to relatively short temporal epochs where behavioral performance is likely to be stable. Incorporating a passive BCI that detects when the user is performing poorly at a primary task, and adapts accordingly may prove to increase overall user performance. Here, we explore the potential for extending an established method to generate continuous estimates of behavioral performance from ongoing neural activity; evaluating the extended method by applying it to the original task domain, simulated driving; and generalizing the method by applying it to a BCI-relevant perceptual discrimination task. Specifically, we used EEG log power spectra and sequential forward floating selection (SFFS) to estimate endogenous changes in behavior in both a simulated driving task and a perceptual discrimination task. For the driving task the average correlation coefficient between the actual and estimated lane deviation was 0.37 ± 0.22 (μ ± σ). For the perceptual discrimination task we generated estimates of accuracy, reaction time, and button press duration for each participant. The correlation coefficients between the actual and estimated behavior were similar for these three metrics (accuracy = 0.25 ± 0.37, reaction time = 0.33 ± 0.23, button press duration = 0.36 ± 0.30). These findings illustrate the potential for modeling time-on-task decrements in performance from concurrent measures of neural activity.

No MeSH data available.


Related in: MedlinePlus