{"ID":2869920,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.21346","arxiv_id":"2509.21346","title":"Spiking Neural Networks for Mental Workload Classification with a Multimodal Approach","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2509.21346","url_pdf":"https://arxiv.org/pdf/2509.21346v1","authors":"[\"Jiahui An\",\"Sara Irina Fabrikant\",\"Giacomo Indiveri\",\"Elisa Donati\"]","published":"2025-09-17T15:26:42Z","proceeding":"cs.NE","tasks":"[\"cs.NE\",\"cs.LG\",\"q-bio.BM\"]","methods":"[]","has_code":false}