Spiking Neural Networks for Mental Workload Classification with a Multimodal Approach

• URL: http://arxiv.org/abs/2509.21346v1
• Date: Wed, 17 Sep 2025 15:26:42 GMT
• Title: Spiking Neural Networks for Mental Workload Classification with a Multimodal Approach
• Authors: Jiahui An, Sara Irina Fabrikant, Giacomo Indiveri, Elisa Donati,
• Abstract summary: Event-driven spiking neural networks (SNNs) offer a promising alternative for low-power, fast, event-driven processing.<n> multimodal integration improves accuracy, with SNN performance comparable to the machine learning (ML) one.<n>Findings position event-based processing as a promising solution for low-latency, energy efficient workload monitoring.
• Score: 1.426230202973353
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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