Spiking Neural Networks for Mental Workload Classification with a Multimodal Approach
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909806519910400 |
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| author | An, Jiahui Fabrikant, Sara Irina Indiveri, Giacomo Donati, Elisa |
| author_facet | An, Jiahui Fabrikant, Sara Irina Indiveri, Giacomo Donati, Elisa |
| contents | Accurately assessing mental workload is crucial in cognitive neuroscience, human-computer interaction, and real-time monitoring, as cognitive load fluctuations affect performance and decision-making. While Electroencephalography (EEG) based machine learning (ML) models can be used to this end, their high computational cost hinders embedded real-time applications. Hardware implementations of spiking neural networks (SNNs) offer a promising alternative for low-power, fast, event-driven processing. This study compares hardware compatible SNN models with various traditional ML ones, using an open-source multimodal dataset. Our results show that multimodal integration improves accuracy, with SNN performance comparable to the ML one, demonstrating their potential for real-time implementations of cognitive load detection. These findings position event-based processing as a promising solution for low-latency, energy efficient workload monitoring in adaptive closed-loop embedded devices that dynamically regulate cognitive load. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21346 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spiking Neural Networks for Mental Workload Classification with a Multimodal Approach An, Jiahui Fabrikant, Sara Irina Indiveri, Giacomo Donati, Elisa Neural and Evolutionary Computing Machine Learning Biomolecules Accurately assessing mental workload is crucial in cognitive neuroscience, human-computer interaction, and real-time monitoring, as cognitive load fluctuations affect performance and decision-making. While Electroencephalography (EEG) based machine learning (ML) models can be used to this end, their high computational cost hinders embedded real-time applications. Hardware implementations of spiking neural networks (SNNs) offer a promising alternative for low-power, fast, event-driven processing. This study compares hardware compatible SNN models with various traditional ML ones, using an open-source multimodal dataset. Our results show that multimodal integration improves accuracy, with SNN performance comparable to the ML one, demonstrating their potential for real-time implementations of cognitive load detection. These findings position event-based processing as a promising solution for low-latency, energy efficient workload monitoring in adaptive closed-loop embedded devices that dynamically regulate cognitive load. |
| title | Spiking Neural Networks for Mental Workload Classification with a Multimodal Approach |
| topic | Neural and Evolutionary Computing Machine Learning Biomolecules |
| url | https://arxiv.org/abs/2509.21346 |