Spiking Neural Networks for Mental Workload Classification with a Multimodal Approach
Year: 2025
Type: conference-paper
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.
Authors: Jiahui An, Sara Irina Fabrikant, Giacomo Indiveri, Elisa Donati
Institutions: University of Zurich, ETH Zurich, Institute of Neuroinformatics
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4414648828