Cross-Subject Cognitive Workload Recognition Based on EEG and Deep Domain Adaptation

Regarding cognitive workload recognition (CWR), electroencephalography (EEG) signals are nonstationary across time and vary from different subjects, thus hindering the cross-subject recognition performance. Although subject calibration or collecting massive training data may ease the above problem, it is generally time-consuming and expensive. In this article, we propose a deep domain adaptation (DDA) scheme for EEG-based cross-subject CWR, using the knowledge from the existing subjects (source domain) to improve the recognition performance of a new subject (target domain). Precisely, the proposed DDA method composes four modules, EEG features extractor, label classifier, feature distribution alignment, and domain discriminator. The model starts with the EEG feature extractor to learn the shallow feature representation for both domains. The label classifier further learns the deep representation and trains the classifier supervised. Finally, the feature distribution alignment matches the shallow feature distribution discrepancy, and the domain discriminator matches the deep distribution discrepancy by the adversarial training with the feature extractor. It not only learns domain-invariant features but also achieves robust domain-adaptive cross-subject recognition results in an end-to-end training framework. We conduct experiments to classify low and high workloads on a self-designed EEG dataset and one public EEG dataset. Experimental results demonstrate that our DDA scheme significantly outperforms the baselines and other state-of-the-art methods with improvements of 2%-9% in terms of recognition accuracy.