Monitoring mental workload by EEG during a game in Virtual Reality

During an activity, knowing the mental workload (MWL) of the user allows to improve the Human-Machine Interactions (HMI). Indeed, the MWL has an impact on the individual and its interaction with the environment. Monitoring it is therefore a crucial issue. In this context, we have created the virtual game Back to Pizza which is based on the N-back task (commonly used for measuring MWL). In this more playful variant, users must carry out orders from customers of a pizza food truck. It is an interactive game that involves the audience of the IHM'23 conference, choosing several parameters like the number of ingredients. During this experience, the objective is to measure MWL in real time through an ElectroEncephaloGraph (EEG) and visual feedback on MWL level is given to the audience. With this demonstration, we propose to present a concept of a virtual interactive game that measures MWL in real time.Comment: IHM'23 - 34e Conf{\'e}rence Internationale Francophone sur l'Interaction Humain-Machine, AFIHM; Universit{\'e} de Technologie de Troyes, Apr 2023, Troyes, Franc

Subjective mental workload modeling in tests involving different cognitive functions

Over time, mental workload (MWL) has become a major topic of scientific discussions. Nevertheless, there is no reference model involving standardized cognitive tests with distinct levels of difficulty and allowing to consider the correlation between task performance and MWL based on these levels. Therefore, we have developed an innovative protocol to model the variation of self-reported MWL (via the NASA-TLX and Workload Profile questionnaires) for tests requiring different cognitive functions (memory, mental inhibition, mental flexibility, and divided attention). Each of these tasks is an adaptation of existing cognitive tests, for which three equivalent difficulty levels were established in terms of impact on performance.The results showed that our difficulty levels allowed distinct classes of MWL and that it co-varied negatively with the performance across difficulty. Our protocol has thus provided new benchmarks that can be useful in many domains where it’s essential to be able to infer MWL from test performance

Inter-tasks transferability of a subjective cognitive load classification model

Studying cognitive load (CL) presents several challenges, including the need for accurate and generic CL classification models. Previous studies have solely relied on physiological data to measure CL, as in Appel et al. (2021). However, what about subjective CL measure?Thus, we proposed a three-class classification model of subjective CL through five cognitive tasks: N-back, Corsi, Go/No-Go, WCST, and Dual task (Louis et al. 2023). For this conference, we examined whether a model trained on a Task A could accurately predict the subjective CL classes of a Task B.Firstly, the results showed that Corsi was the most effective task for classifying subjective CL based only on performance and complexity levels, achieving 80% accuracy. Moreover, a classification model trained on N-back, WCST, and Dual task could predict the subjective CL classes of Corsi with over 70% accuracy. This perspective would save time in setting up and training a classification model

Cognitive tasks and combined statistical methods to evaluate, model, and predict mental workload

Mental workload (MWL) is a concept that is used as a reference for assessing the mental cost of activities. In recent times, challenges related to user experience are determining the expected MWL value for a given activity and real-time adaptation of task complexity level to achieve or maintain desired MWL. As a consequence, it is important to have at least one task that can reliably predict the MWL level associated with a given complexity level. In this study, we used several cognitive tasks to meet this need, including the N-Back task, the commonly used reference test in the MWL literature, and the Corsi test. Tasks were adapted to generate different MWL classes measured via NASA-TLX and Workload Profile questionnaires. Our first objective was to identify which tasks had the most distinct MWL classes based on combined statistical methods. Our results indicated that the Corsi test satisfied our first objective, obtaining three distinct MWL classes associated with three complexity levels offering therefore a reliable model (about 80% accuracy) to predicted MWL classes. Our second objective was to achieve or maintain the desired MWL, which entailed the use of an algorithm to adapt the MWL class based on an accurate prediction model. This model needed to be based on an objective and real-time indicator of MWL. For this purpose, we identified different performance criteria for each task. The classification models obtained indicated that only the Corsi test would be a good candidate for this aim (more than 50% accuracy compared to a chance level of 33%) but performances were not sufficient to consider identifying and adapting the MWL class online with sufficient accuracy during a task. Thus, performance indicators require to be complemented by other types of measures like physiological ones. Our study also highlights the limitations of the N-back task in favor of the Corsi test which turned out to be the best candidate to model and predict the MWL among several cognitive tasks

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