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arXiv Machine Learning By Stefanos Gkikas, Christian Arzate Cruz, Calvin Joseph, Giorgos Giannakakis, Raul Fernandez Rojas

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

Read the original on arXiv Machine Learning →

The paper presents a modality‑agnostic, hierarchical Transformer framework for assessing cognitive workload using heterogeneous biosignals. In a pilot study, the authors evaluated all 31 combinations of five modalities (ECG, EDA, RESP, SpO₂, EEG) across three tasks (IQ, MATH, GAME) and found that EEG alone performed best, while adding more modalities did not consistently improve results. The full five‑modality model achieved the highest average accuracy (73.02% on IQ, 68.08% overall) and reduced model size by about 50% compared to late‑fusion approaches.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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