Efficient Workload Classification based on Ignored Auditory Probes: A Proof of Concept

• Published in: Frontiers in human neuroscience Vol. 10; p. 519
• Main Authors: Roy, Raphaëlle N, Bonnet, Stéphane, Charbonnier, Sylvie, Campagne, Aurélie
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 13-10-2016; Frontiers; Frontiers Media S.A
• Subjects: Algorithms; Auditory Evoked Potentials; Behavior; Classification; Cognitive science; Correlation analysis; EEG; Electroencephalography; Event-related potentials; Interfaces; MATB; Memory; Neuroergonomics; Neuroscience; Physiology; Probes; Signal processing; Spatial Filtering; Workload; Workloads; France; workload; auditory evoked potentials; classification; spatial filtering
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2016.00519

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.