Spiking Neural Networks for Mental Workload Classification with a Multimodal Approach
Year: 2025
Type: conference-paper
Abstract: Accurately assessing mental workload is crucialin 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 demands.
DOI: 10.5167/uzh-280937
Authors: An, Jiahui, Fabrikant, Sara, Indiveri, Giacomo, Donati, Elisa
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7114769032
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