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.
Highlights
Brain-Computer Interaction (BCI) technologies that enable computer systems to adapt to the current cognitive or affective state of the user provide a promising avenue for developing systems that will improve human interaction with computers, the environment, and even each other (Zander and Kothe, 2011; Lance et al, 2012)
The results of this study suggest that opportunistic identification of time-on-task performance decrements within an event-based BCI is both feasible and advantageous
The TaskInduced Fatigue Scale (TIFS) revealed a significant task type effect for boredom and visual fatigue (p < 0.01), where the driving task was perceived as more boring, and the rapid serial visual presentation (RSVP) task was perceived as inducing more visual fatigue
Summary
Brain-Computer Interaction (BCI) technologies that enable computer systems to adapt to the current cognitive or affective state of the user provide a promising avenue for developing systems that will improve human interaction with computers, the environment, and even each other (Zander and Kothe, 2011; Lance et al, 2012). Among the broad range of BCI technologies, the majority of systems and approaches have been within the active and reactive BCI paradigms (see Zander and Kothe, 2011 for review) These two classes of BCIs seek to decode volitionally induced or externally elicited patterns of neural activity over a relatively short timescale, on the order of milliseconds to seconds. Active and reactive BCI technologies have shown limited success outside of specific patient populations One reason for this is a lack of robustness, partly due to the non-stationarity of neural signals (Von Bünau et al, 2009; Liyanage et al, 2013). In addition to large inter-session variability, fatigue and other sources of time-on-task decrements in performance can be reflected as a non-stationarity in the neural activity. We investigate the possibility of addressing this form of non-stationarity through the incorporation of an algorithm designed to identify fatigue-based decrements in performance
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