MULTI-SOURCE DOMAIN GENERALIZATION FOR ECG-BASED COGNITIVE LOAD ESTIMATION: ADVERSARIAL INVARIANT AND PLAUSIBLE UNCERTAINTY LEARNING

Electrocardiography (ECG) for objective cognitive load estimation gained increasing attention, and offers a more feasible and non-invasive alternative to traditional methods such as electroencephalography (EEG). Despite the promise of ECG signal, application in real-world scenarios is hampered by the domain shift present in data collected in controlled environments versus real-world settings. We propose a novel plug-in generalizable framework, CogDG-ECG, assessed on a first-introduced multi-source domain generalization (MSDG) protocol for generalized cognitive load estimation. CogDG-ECG bridges the domain gap by extracting domain-invariant features through adversarial learning, and estimating instance-specific unseen features by synthesizing plausible feature statistical variations. A new benchmark based on three datasets and MSDG protocol was introduced, which demonstrates the superiority of our proposed method.