Towards a Unified Modality-Agnostic Multimodal Framework for Cognitive Workload Assessment

Abstract:Cognitive workload reflects the mental effort required during task performance and is central to the design of adaptive human-machine systems. The use of biosignals to measure cognitive workload has been extensively researched and documented; however, studies examining the effects of combining heterogeneous biosignal modalities for this purpose remain limited. To provide insight into this area, we developed a unified, modality-agnostic, hierarchical Transformer-based architecture to process heterogeneous biosignal modalities within a single model. We use this framework in a pilot study evaluating all $31$ possible combinations of five modalities: Electrocardiogram (ECG), Electrodermal Activity (EDA), Respiration (RESP), Peripheral Oxygen Saturation (SpO$_2$), and Electroencephalogram (EEG), under leave-one-subject-out validation across three cognitively distinct tasks: abstract reasoning (IQ), arithmetic problem solving (MATH), and a game task (GAME). In this pilot setting, the results suggest that: (i) EEG is the strongest single modality, ranking highest in IQ, GAME, and the pooled ALL setting, where samples from all three tasks are combined; (ii) adding more modalities does not consistently improve performance; (iii) the full five-modality combination achieves the highest \textit{Average} score of $73.02%$ on IQ and $68.08%$ when the \textit{Average} scores are averaged over the four evaluation settings: IQ, MATH, GAME, and ALL; and (iv) the proposed method reduces model size by approximately $50%$ compared with late-fusion alternatives while maintaining a lower inference time.

Machine Learning (cs.LG); Human-Computer Interaction (cs.HC)

arXiv:2609.20199 [cs.LG]

(or arXiv:2609.20199v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.20199 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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