Abstract
A passive Brain Computer Interface (BCI) is a system that responds to the spontaneously produced brain activity of its user and could be used to develop interactive task support. A human-machine system that could benefit from brain-based task support is the driver-car interaction system. To investigate the feasibility of such a system to detect changes in visuomotor workload, 34 drivers were exposed to several levels of driving demand in a driving simulator. Driving demand was manipulated by varying driving speed and by asking the drivers to comply to individually set lane keeping performance targets. Differences in the individual driver's workload levels were classified by applying the Common Spatial Pattern (CSP) and Fisher's linear discriminant analysis to frequency filtered electroencephalogram (EEG) data during an off line classification study. Several frequency ranges, EEG cap configurations, and condition pairs were explored. It was found that classifications were most accurate when based on high frequencies, larger electrode sets, and the frontal electrodes. Depending on these factors, classification accuracies across participants reached about 95% on average. The association between high accuracies and high frequencies suggests that part of the underlying information did not originate directly from neuronal activity. Nonetheless, average classification accuracies up to 75–80% were obtained from the lower EEG ranges that are likely to reflect neuronal activity. For a system designer, this implies that a passive BCI system may use several frequency ranges for workload classifications.
Highlights
In contrast to an active Brain-Computer Interface (BCI) which allows users to engage in volitional thought control of a device, several BCI researchers have proposed to advance humancomputer interaction by triggering machine actions based on inferences of the user’s current mental state, known as passive BCI (Kohlmorgen et al, 2007; Cutrell and Tan, 2008; Zander et al, 2010; Zander and Kothe, 2011)
The aim of the study was to investigate the feasibility of using EEG for monitoring the level of visuomotor workload in a driving environment, which can potentially be used by an user adaptive driver support system
Since increasing steering effort normally decreases swerving behavior within the driving lane given a particular speed, participants were presented with three explicit swerving performance targets represented as the standard deviation of the lateral position of the car with respect to the driving lane
Summary
In contrast to an active Brain-Computer Interface (BCI) which allows users to engage in volitional thought control of a device, several BCI researchers have proposed to advance humancomputer interaction by triggering machine actions based on inferences of the user’s current mental state, known as passive BCI (Kohlmorgen et al, 2007; Cutrell and Tan, 2008; Zander et al, 2010; Zander and Kothe, 2011). Kohlmorgen et al (2007) showed that it is possible to classify mental workload elicited by a secondary task mimicking cognitive processes in a real driving environment. These classifications were used to switch on and off a tertiary task that mimicked an interaction with the vehicle’s electrical devices that in turn improved performance on the secondary task. Mental underload and overload both represent compromised functional states during which a breakdown of primary task performance is more likely (e.g., Hockey, 1997, 2003; see Brookhuis and de Waard, 2010). Preventing these hazardous functional states by maintaining mental workload or task demand within an acceptable range in real-time has been the central goal of adaptive automation since the seventies (Chu and Rouse, 1979; Hancock and Chignell, 1988; Rouse, 1988; Parasuraman et al, 1992; Kaber and Prinzel, 2006)
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