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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.12162 |
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| _version_ | 1866914113370718208 |
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| author | Balti, Aymen Wade, Assane Oujbara, Abdelatif A., M. Aziz-Alaoui Bellarabi, Hicham Dutertre, Frederic Ambrosio, Benjamin |
| author_facet | Balti, Aymen Wade, Assane Oujbara, Abdelatif A., M. Aziz-Alaoui Bellarabi, Hicham Dutertre, Frederic Ambrosio, Benjamin |
| contents | We introduce a novel framework for quantifying mental and emotional states over time by combining virtual reality (VR) exposure with EEG recordings. Participants experienced a stress-inducing work scenario in VR, originally designed as a training tool for bank employees, providing a controlled proxy for high-stakes situations. This setup enables integration of subjective emotional self-assessments with objective neural data, from which an algorithm was efficiently used to infer emotional states. Building on these measurements, we propose possible mathematical models to capture the temporal dynamics of mental states, offering a quantitative approach to studying emotional processing and informing adaptive training in complex environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12162 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantifying Mental States in Work Environment: Mathematical Perspectives Balti, Aymen Wade, Assane Oujbara, Abdelatif A., M. Aziz-Alaoui Bellarabi, Hicham Dutertre, Frederic Ambrosio, Benjamin Neurons and Cognition We introduce a novel framework for quantifying mental and emotional states over time by combining virtual reality (VR) exposure with EEG recordings. Participants experienced a stress-inducing work scenario in VR, originally designed as a training tool for bank employees, providing a controlled proxy for high-stakes situations. This setup enables integration of subjective emotional self-assessments with objective neural data, from which an algorithm was efficiently used to infer emotional states. Building on these measurements, we propose possible mathematical models to capture the temporal dynamics of mental states, offering a quantitative approach to studying emotional processing and informing adaptive training in complex environments. |
| title | Quantifying Mental States in Work Environment: Mathematical Perspectives |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2509.12162 |