# Neuro task-manager: enhancing well-being in the workplace using passive BCI

Emile Savalle, Marc J.-M. Macé, Léa Pillette, Ferran Argelaguet, Anatole Lécuyer, Thorsten O Zander, Marius Klug

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Emile Savalle, Marc J.-M. Macé, Léa Pillette, Ferran Argelaguet, Anatole Lécuyer, et al. Neuro task-manager: enhancing well-being in the workplace using passive BCI. NAT 2025 - Neuroadaptive technology Conference, Apr 2025, Berlin, Germany. pp.1-3. hal-05050027

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Submitted on 28 Apr 2025

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Distributed under a Creative Commons Attribution 4.0 International License
NEURO TASK-MANAGER: ENHANCING WELL-BEING
IN THE WORKPLACE USING PASSIVE BCI

Keywords: passive BCI, Virtual Reality

Emile Savalle¹, Marc J-M Macé¹, Léa Pillette¹, Ferran Argelaguet², Anatole Lécuyer²,
Thorsten O. Zander³, Marius Klug⁴

¹Univ. Rennes, Inria, CNRS, IRISA - F35000 Rennes, France.

²Inria, Univ. Rennes, IRISA, CNRS - F35000 Rennes, France.

³BTU Cottbus-Senftenberg, Chair for Neuroadaptive Human-Computer Interaction

⁴BTU Cottbus-Senftenberg, Young Investigator Group Intuitive XR, Chair for Neuroadaptive Human-Computer Interaction

**ABSTRACT:**

This study explores the feasibility of using passive brain-computer interfaces (BCIs) to optimise task assignments in the workplace and address fatigue, stress, and alienation caused by task misalignment. We developed a virtual factory with three tasks designed to elicit unique executive functions (Shifting, Updating, and Inhibition). Using passive BCI, we calibrated two user models: either based on cognitive workload or behavioral performance, each adapting task selection to the participant. Results revealed that the performance-based model caused more fatigue while maintaining performance comparable to the workload-based model, highlighting the potential of passive BCIs to address some workplace-related issues.

**INTRODUCTION:**

In recent years, mental health in the workplace has been a significant concern. If a job does not fit the qualifications and abilities of its assigned worker, it can induce various problems. A task causing too high a workload will lead to stress and fatigue, and one causing a workload too low produces boredom and/or alienation. These problems can arise from being assigned to a task purely based on performance. In this study, we investigate if a task assigned by a user model calibrated on cognitive workload, measured by a passive-BCI and designed to keep workload at an optimum level, is preferred by a user and reduces fatigue, compared to a model based on performance.

**MATERIALS AND METHODS:**

In our experiment, 20 participants were equipped with a 64-electrode Live Amp EEG cap from Brain Products and an HTC Vive Pro Eye virtual reality (VR) headset. First, we calibrated a workload classifier using the Sparkles paradigm [2]. The paradigm was displayed in VR to ensure consistency between calibration and application [3]. Data resulting from this paradigm was used to calibrate the workload classifier. Participants with 55% or less accuracy, computed on a 5-fold cross-validation of the calibration data on the two-class classifier, were removed from the analysis. The remaining participants (15) had an average accuracy of 64.8% (±7.3%). Then participants were immersed in the virtual environment (Figure 1). The environment consisted of a virtual factory, equipped with a screen displaying information and a conveyor belt conveying objects. Three tasks were designed (each with 3 difficulty levels) to reveal individual differences. In their paper [1], Miyake & al. evaluated different executive functions and showed that three main executive functions, i.e., Shifting (mental set shifting), Updating (information updating and monitoring), and Inhibition (inhibition of prepotent responses), were clearly separable and could elicit individual differences. Based on tasks traditionally used to elicit these executive functions [1], the following three tasks

<figure><img src="image_2.png" /><figcaption>Figure 1: Virtual setup, including a screen displaying information, the conveyor belt providing objects for the different tasks and bins for sorting objects.</figcaption></figure>
were designed:

1. Updating (U) - N-back task: Participants had to select objects similar to previous ones.

2. Shifting (S) - ColorShape task: Participants had to sort objects between two bins based on changing instructions

3. Inhibition (I)- Go/Nogo task: Participants had to select objects that fitted an instruction under severe time pressure, 70% of objects fitted the instructions.

Participants experienced all these tasks and difficulties in a calibration phase, each task being done for 2 minutes. During these tasks, workload from the classifier and performance were measured. Using this data, we calibrated two user models:

- Workload model: each task in each difficulty was given a score between 0 and 1, rewarding when the workload was near the average workload and penalising if the workload was too high or too low in a task.

- Performance model: each task in each difficulty was scored between 0 and 1, rewarding when performance was high.

Once calibrated, each model generated a distinct solution composed of three distinct tasks, each assigned a unique difficulty level. The solution was selected by choosing the combination giving the highest total score.

Then, participants went through a model evaluation phase, during which they performed the tasks selected by each model. Before and after each task, they answered the VAS-F fatigue questionnaire.

## RESULTS:

First, to study the impact of each task of the validation phase, we observed the evolution of fatigue by observing the change in answers to the VAS-F questionnaire before and after each task. The fatigue questionnaire showed that using the performance model increased fatigue significantly more than the workload model. T-test statistical analysis revealed a significant difference between the use of the two models [$t = -2.71, p = 0.009$], workload: $M = +12.32, SD = \pm15.33$, performance: $M = +26.31, SD = \pm26.35$. Then, we calculated a change in performance between the two models. A t-test statistical analysis revealed a non-significant decrease in performance in the workload model ($M = 62\%, SD = 14\%$) compared to the performance model ($M = 67\%, SD = 15\%$). However, the workload data used to set the workload model did not align with expectations: it showed no link with the changes in task difficulty and did not correlate with subjective workload ratings. These results should be interpreted cautiously and warrant further experimentation to confirm their validity.

## CONCLUSION:

In this study, we've explored the feasibility of using a passive BCI in the workplace to address problems caused by workload and misalignment between workers and their tasks, namely alienation or exhaustion. We developed a virtual factory and implemented 3 tasks designed to elicit individual differences. Using performance and workload resulting from these tasks, we calibrated two user models, one based on workload, the other on performance, to compare task assignments based on performance and fatigue. Our results demonstrate that the workload-based user model significantly reduced fatigue compared to the performance-based model, without causing a substantial decrease in task performance. While these findings are promising, the underlying workload data requires further investigation before conclusions can be drawn. Additional experimentation is needed to confirm the robustness and reliability of this approach. These findings highlight the potential of passive BCI technology for optimising task assignments in workplace environments and improving user well-being while maintaining productivity. This study shows that it is now feasible to leverage passive BCIs outside laboratory settings for real-world applications, offering a promising solution to enhance workplace satisfaction and efficiency by tailoring tasks to individual cognitive profiles.
REFERENCES:

[1] Miyake, Akira, et al. (2000). *The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis*. Cognitive psychology 41.1, 49-100.

[2] Zhang, X., Krol, L. R., & Zander, T. O. (2018). *Towards Task-Independent Workload Classification: Shifting from Binary to Continuous Classification*. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, 556-561.

[3] Gherman, Diana E., and Thorsten O. Zander. (2022). *An investigation of a passive BCI's performance in different user postures and presentation modalities*. NAT 2022.