Abstract
Mental workload is a mental state that is currently one of the main research focuses in neuroergonomics. It can notably be estimated using measurements in electroencephalography (EEG), a method that allows for direct mental state assessment. Auditory probes can be used to elicit event-related potentials (ERPs) that are modulated by workload. Although, some papers do report ERP modulations due to workload using attended or ignored probes, to our knowledge there is no literature regarding effective workload classification based on ignored auditory probes. In this paper, in order to efficiently estimate workload, we advocate for the use of such ignored auditory probes in a single-stimulus paradigm and a signal processing chain that includes a spatial filtering step. The effectiveness of this approach is demonstrated on data acquired from participants that performed the Multi-Attribute Task Battery – II. They carried out this task during two 10-min blocks. Each block corresponded to a workload condition that was pseudorandomly assigned. The easy condition consisted of two monitoring tasks performed in parallel, and the difficult one consisted of those two tasks with an additional plane driving task. Infrequent auditory probes were presented during the tasks and the participants were asked to ignore them. The EEG data were denoised and the probes’ ERPs were extracted and spatially filtered using a canonical correlation analysis. Next, binary classification was performed using a Fisher LDA and a fivefold cross-validation procedure. Our method allowed for a very high estimation performance with a classification accuracy above 80% for every participant, and minimal intrusiveness thanks to the use of a single-stimulus paradigm. Therefore, this study paves the way to the efficient use of ERPs for mental state monitoring in close to real-life settings and contributes toward the development of adaptive user interfaces.
Highlights
Mental workload is frequently defined as task difficulty and the associated mental effort (Gevins and Smith, 2007)
There was a significant effect of workload on this performance score (t = 2.99, p < 0.05), the participants’ performance was significantly degraded in the high workload condition compared to the low workload condition (m1_perf = 0.33; sd1_perf = 0.12; m2_perf = 0.43; sd2_perf = 0.12)
In order to do so, a single-stimulus paradigm similar to that of Allison and Polich (2008) was used, along with a processing chain that included a canonical correlation analysis filtering (CCA) spatial filtering step. The participants rated their effort as significantly higher for the high workload condition than for the low one and exhibited a decrease in performance in the high workload condition compared to the low workload condition akin to that observed by Fournier et al (1999)
Summary
Mental workload is frequently defined as task difficulty and the associated mental effort (Gevins and Smith, 2007). It is of critical interest to better assess this state to the human factor community who aims at developing smart technologies that enhance operator’s safety and performance. Workload Classification Using Ignored Probes (Sternberg, 1969), as well as with the number of tasks to perform in parallel (Cain, 2007). Behavioral responses are not always enough for mental state monitoring (MSM) systems, mainly due to their latency of occurrence, and to the fact that some mental states are not necessarily or systematically reflected by a specific response. Physiological data give more insight into the operator’s state, especially electroencephalography (EEG), a method that allows for direct mental state assessment. The use of physiological markers derived from the cerebral activity for human factor purposes has given rise to a new field: neuroergonomics (Parasuraman et al, 2012)
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