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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

Flowchart of data preprocessing and model fitting. Central box encapsulates the iterative process of feature (channel) selection via the SFFS algorithm. For the standard modeling scheme the feature selection component is replaced by the fixed midline montage: Fz, Cz, Pz, and Oz.
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Figure 2: Flowchart of data preprocessing and model fitting. Central box encapsulates the iterative process of feature (channel) selection via the SFFS algorithm. For the standard modeling scheme the feature selection component is replaced by the fixed midline montage: Fz, Cz, Pz, and Oz.

Mentions: Moving-average power spectra were based on an approach described by Lin et al. (2005a). Briefly, the power spectral density (PSD) estimates were calculated in sliding 750-point epochs (~3 s) with a 500-point step size (~2 s). Each epoch was subdivided into 125-point Hanning windows with a 25-point step size. A 256-point FFT was then used to calculate the power spectrum for each window and a 5th order median filter was applied across windows for artifact mitigation. The windowed spectra were then averaged and converted into a logarithmic scale to produce the time-varying PSD estimate for each channel. Frequencies between 1 and 40 Hz were kept for subsequent analysis. Finally, the power estimates at these frequencies were smoothed with a 90 s mean filter in the identical fashion as the behavioral metrics described above. Figure 2 outlines the sequence of steps in the EEG preprocessing, behavior integration, and model building components of the analysis.


Estimating endogenous changes in task performance from EEG.

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

Flowchart of data preprocessing and model fitting. Central box encapsulates the iterative process of feature (channel) selection via the SFFS algorithm. For the standard modeling scheme the feature selection component is replaced by the fixed midline montage: Fz, Cz, Pz, and Oz.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Flowchart of data preprocessing and model fitting. Central box encapsulates the iterative process of feature (channel) selection via the SFFS algorithm. For the standard modeling scheme the feature selection component is replaced by the fixed midline montage: Fz, Cz, Pz, and Oz.
Mentions: Moving-average power spectra were based on an approach described by Lin et al. (2005a). Briefly, the power spectral density (PSD) estimates were calculated in sliding 750-point epochs (~3 s) with a 500-point step size (~2 s). Each epoch was subdivided into 125-point Hanning windows with a 25-point step size. A 256-point FFT was then used to calculate the power spectrum for each window and a 5th order median filter was applied across windows for artifact mitigation. The windowed spectra were then averaged and converted into a logarithmic scale to produce the time-varying PSD estimate for each channel. Frequencies between 1 and 40 Hz were kept for subsequent analysis. Finally, the power estimates at these frequencies were smoothed with a 90 s mean filter in the identical fashion as the behavioral metrics described above. Figure 2 outlines the sequence of steps in the EEG preprocessing, behavior integration, and model building components of the analysis.

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