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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

Continuous measure of driving and RSVP (reaction time) behavior for participant S08. (A) Actual and estimated absolute lane deviation (meters) over the 6 driving blocks. (B) Actual and estimated normalized RT (ms) over the 6 RSVP blocks. Horizontal bars indicate experiment blocks.
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Figure 7: Continuous measure of driving and RSVP (reaction time) behavior for participant S08. (A) Actual and estimated absolute lane deviation (meters) over the 6 driving blocks. (B) Actual and estimated normalized RT (ms) over the 6 RSVP blocks. Horizontal bars indicate experiment blocks.

Mentions: In addition to target detection accuracy, we wanted to quantify the relationship between the PSD and the two other behavioral measures within the RSVP task. To accomplish this, we used the same adaptive modeling scheme described above to fit regression models and construct estimates for both normalized RT and button press duration. Figure 7B shows the actual and estimated RT for one participant in the RSVP task, while Figure 8B shows the actual and estimated button press duration for another participant. Here, estimates of lane deviation from their corresponding driving tasks are included for comparison (Figures 7A, 8A). Interestingly, our adaptive approach was able to produce significant behavioral estimates for both the RT and duration metrics in the majority of participants (RT n = 14, duration n = 17). The average correlation coefficients from these behavioral estimates were similar (RT R = 0.332 ± 0.225, duration R = 0.360 ± 0.302) to the normalized accuracy metric.


Estimating endogenous changes in task performance from EEG.

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

Continuous measure of driving and RSVP (reaction time) behavior for participant S08. (A) Actual and estimated absolute lane deviation (meters) over the 6 driving blocks. (B) Actual and estimated normalized RT (ms) over the 6 RSVP blocks. Horizontal bars indicate experiment blocks.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Continuous measure of driving and RSVP (reaction time) behavior for participant S08. (A) Actual and estimated absolute lane deviation (meters) over the 6 driving blocks. (B) Actual and estimated normalized RT (ms) over the 6 RSVP blocks. Horizontal bars indicate experiment blocks.
Mentions: In addition to target detection accuracy, we wanted to quantify the relationship between the PSD and the two other behavioral measures within the RSVP task. To accomplish this, we used the same adaptive modeling scheme described above to fit regression models and construct estimates for both normalized RT and button press duration. Figure 7B shows the actual and estimated RT for one participant in the RSVP task, while Figure 8B shows the actual and estimated button press duration for another participant. Here, estimates of lane deviation from their corresponding driving tasks are included for comparison (Figures 7A, 8A). Interestingly, our adaptive approach was able to produce significant behavioral estimates for both the RT and duration metrics in the majority of participants (RT n = 14, duration n = 17). The average correlation coefficients from these behavioral estimates were similar (RT R = 0.332 ± 0.225, duration R = 0.360 ± 0.302) to the normalized accuracy metric.

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